Apr 2, 2008 - Faculty of Business, Technology and Sustainable Development ... their time and support to help me during the completion of this research.
Organisational Learning, Innovation and Performance in Family-Controlled Manufacturing Small and Medium-Sized Enterprises (SMEs) in Australia
A dissertation submitted in fulfilment of the requirements for the degree of Doctor of Philosophy
By
Pradeep Dharmadasa
Faculty of Business, Technology and Sustainable Development Bond University Queensland Australia
MARCH 2009
STATEMENT OF ORIGINALITY To the best of my knowledge and belief, the work presented in this dissertation is original, except as acknowledged in the text. All sources used in the study have been cited and no attempt has been made to project the contributions of other researchers as my own. Further, the material has not been submitted, either in whole or in part for a degree at this or any other university.
Pradeep Dharmadasa Faculty of Business, Technology and Sustainable Development Bond University Gold Coast, Queensland 4229 Australia March 2009
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ACKNOWLEDGMENTS I would like to extend my sincere gratitude to those who have been so generous with their time and support to help me during the completion of this research.
First and foremost, I would like to thank my supervisor Professor Ken Moores, for his invaluable guidance, encouragement, comments and constructive criticisms, and unabated enthusiasm throughout my candidature. I also thank Dr Justin Craig, Associate Professor and Head of the Global Strategy, Entrepreneurship and Family Business Department for his invaluable support and guidance.
My PhD candidature was supported by a Postgraduate Award from the National Centre for Advanced Studies in Humanities and Social Sciences in Sri Lanka as well as a Postgraduate Award from Bond University in Australia. The support of both institutions is appreciatively acknowledged.
Thank you to the staff at the Bond University Library and the Faculty of Business, Technology and Sustainable Development for their support and assistance. I also would like to extend my thanks to the Australian Statistician for granting the permission me to use data from the Business Longitudinal Survey for this research. I am appreciative of the support from my fellow PhD candidates, especially Wayne Irava, Chiou See Anderson, Mark Yupitun, Kevin Tang and Abdullah Al-Hatmi. Finally, my special gratitude goes to my loving parents, wife and little daughter Sheroni for their support and patience.
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ABSTRACT Organisational learning has been identified as a lasting source of competitive advantage in uncertain environments. Plentiful research has highlighted that knowledge and skills and the capabilities they develop are strategic resources and that effective utilisation of these resources enhances firm innovation and performance. However, in spite of this widespread recognition, family businesses, specifically family SMEs, have not been the subject of previous research exploring the strategic impact of organisational learning on innovation and firm performance. This research, therefore, sets out a theoretical framework drawing upon organisational learning theory and innovation, and explores the strategic links between organisational learning, innovation and firm performance “within” family SMEs and “between” family and non-family SMEs. The study was undertaken in an Australian context using a sample of 222 manufacturing SMEs comprised of 104 family and 118 non-family SMEs. The data were obtained from the Business Longitudinal Survey conducted by the Australian Bureau of Statistics over the financial years 1995/96 - 1997/98, which provides the most recent available comprehensive longitudinal dataset of SMEs in Australia. The study involved three constructs: organisational learning, innovation and firm performance. Organisational learning was operationalised using commitment to learning, shared vision, and networking. To measure commitment to learning, three variables, employee training, management development, and comparison of performance were used. Shared vision was measured using the presence of formal planning in the firms. Networking was measured using the existence of external networks. The innovation construct was measured using product and process innovation
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intensity, and firm performance was measured by growth of sales and rate of return on total assets. Data were analysed using two tests: regression analysis and the Chow test. Whereas the former test was conducted to explore the direct and indirect effects of organisational learning on innovation and firm performance “within” family SMEs, the latter was conducted to compare those effects “between” family and non-family SMEs. Our “within” results, concerning the direct effects of organisational learning on innovation found that network relationships positively influenced innovation in family SMEs. With regard to the direct effects of organisational learning on performance, we found that management development and formal planning were positively linked with family SMEs’ performance. Moreover, relating to innovation and firm performance, our research
concludes that innovation in family SMEs is positively linked with their performance. In the case of the indirect effect, we found that networks affect firm performance via innovation. With respect to the “between” results, we found that whereas the effects of formal planning and innovation on firm performance of family SMEs were stronger than for non-family SMEs, the effects of employee training and management development on firm performance were stronger in non-family SMEs. Concerning networks, we found a stronger effect of family SMEs’ networks on their innovation than non-family SMEs.
Finally, we re-emphasised the necessity of more scholarly studies linking organisational learning with family business characteristics such as familiness, leadership, ownership, social interactions and organisational process. Keywords: organisational learning; innovation; firm performance; SMEs, family firms and family SMEs.
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TABLE OF CONTENTS STATEMENT OF ORIGINALITY ........................................................................ I ACKNOWLEDGMENTS ................................................................................... II ABSTRACT ................................................................................................... III TABLE OF CONTENTS .................................................................................... V LIST OF TABLES ............................................................................................ IX LIST OF FIGURES .......................................................................................... XI ABBREVIATIONS ......................................................................................... XII 1.
INTRODUCTION AND OVERVIEW ....................................................1-1 1.1 BACKGROUND.........................................................................................1-1 1.2 RESEARCH QUESTIONS............................................................................1-4 1.3 OBJECTIVES OF THE RESEARCH ..............................................................1-6 1.4 SCOPE OF THE RESEARCH .......................................................................1-7 1.5 CONTRIBUTION OF THE RESEARCH .........................................................1-7 1.5.1
FAMILY BUSINESS, ORGANISATIONAL LEARNING AND INNOVATION FIELDS ................................................................................................ 1-7
1.5.2
PRACTITIONERS AND POLICY-MAKERS ................................................ 1-8
1.5.3
RESEARCHERS .................................................................................... 1-9
1.6 RESEARCH METHOD ................................................................................1-9 1.7 STRUCTURE OF THE RESEARCH.............................................................1-10 1.8 CHAPTER SUMMARY .............................................................................1-11
2.
LITERATURE REVIEW ..................................................................2-12 2.1 INTRODUCTION .....................................................................................2-12 2.2 ORGANISATIONAL LEARNING ...............................................................2-12 2.2.1
INTRODUCTION ................................................................................. 2-12
2.2.2
CONCEPT OF ORGANISATIONAL LEARNING ........................................ 2-14
2.2.3
ELEMENTS OF ORGANISATIONAL LEARNING ...................................... 2-19
2.3 ORGANISATIONAL INNOVATION ...........................................................2-22 2.3.1
OVERVIEW OF INNOVATION .............................................................. 2-22
2.3.2
DEFINING INNOVATION ..................................................................... 2-23
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2.3.2.1 THEORETICAL DEFINITIONS ............................................................. 2-23 2.3.2.2 TECHNICAL DEFINITIONS ................................................................. 2-24
2.3.3
TYPES OF INNOVATION...................................................................... 2-26
2.3.4
FACTORS INFLUENCING INNOVATION ................................................ 2-28
2.4 FAMILY BUSINESSES .............................................................................2-30 2.4.1
SIGNIFICANCE OF FAMILY BUSINESSES AND FAMILY BUSINESS RESEARCH ......................................................................................... 2-30
2.4.2
DEFINING FAMILY FIRMS ................................................................... 2-32
2.4.3
UNIQUE NATURE OF FAMILY FIRMS ................................................... 2-38
2.4.4
FAMILY BUSINESS IN AUSTRALIA ...................................................... 2-41 2.4.4.1 INTRODUCTION ................................................................................ 2-41 2.4.4.2 FAMILY SMES ................................................................................. 2-43
2.5 CONCEPTUAL FRAMEWORK AND HYPOTHESES ....................................2-45 2.5.1
INTRODUCTION ................................................................................. 2-45
2.5.2
ORGANISATIONAL LEARNING AND INNOVATION................................ 2-46 2.5.2.1 COMMITMENT TO LEARNING AND INNOVATION .............................. 2-48 2.5.2.2 SHARED VISION AND INNOVATION .................................................. 2-50 2.5.2.3 NETWORKING AND INNOVATION ..................................................... 2-51
2.5.3
ORGANISATIONAL LEARNING AND FIRM PERFORMANCE .................... 2-52
2.5.4
INNOVATION AND FIRM PERFORMANCE ............................................. 2-53
2.5.5
ORGANISATIONAL LEARNING, INNOVATION AND FIRM PERFORMANCE: FAMILY AND NON-FAMILY SMES ..................................................... 2-54
2.6 CHAPTER SUMMARY .............................................................................2-57
3.
RESEARCH METHOD....................................................................3-60 3.1 INTRODUCTION .....................................................................................3-60 3.2 THE RESEARCH DESIGN.........................................................................3-60 3.2.1
DATA COLLECTION – BUSINESS LONGITUDINAL SURVEY.................. 3-60
3.2.2
SAMPLE SELECTION .......................................................................... 3-64
3.2.3
OPERATIONALISATION OF THE VARIABLES ........................................ 3-66 3.2.3.1 ORGANISATIONAL LEARNING .......................................................... 3-68 3.2.3.2 INNOVATION .................................................................................... 3-69 3.2.3.3 FIRM PERFORMANCE ........................................................................ 3-70 3.2.3.4 CONTROL VARIABLES ...................................................................... 3-71
3.3 STATISTICAL TECHNIQUES ....................................................................3-74 3.3.1
MULTIPLE LINEAR REGRESSION ANALYSIS ........................................ 3-74
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3.3.2
CHOW TEST ....................................................................................... 3-77
3.4 CHAPTER SUMMARY .............................................................................3-78
4.
QUANTITATIVE ANALYSIS AND RESULTS .....................................4-79 4.1 INTRODUCTION .....................................................................................4-79 4.2 DEMOGRAPHIC CHARACTERISTICS OF THE FIRMS IN THE BUSINESS LONGITUDINAL SURVEY ......................................................................4-79 4.2.1
INDUSTRY DISTRIBUTION OF FIRMS ................................................... 4-79
4.2.2
FIRM SIZE AND AGE ........................................................................... 4-81
4.2.3
LEGAL STATUS OF FIRMS ................................................................... 4-83
4.2.4
OWNERSHIP OF FIRMS ....................................................................... 4-84
4.2.5
CONTINUING FIRMS ........................................................................... 4-85
4.3 DESCRIPTIVE STATISTICS OF THE SAMPLED FIRMS ...............................4-86 4.4 RESULTS OF THE STUDY ........................................................................4-93 4.4.1
LEARNING, INNOVATION AND PERFORMANCE: WITHIN FAMILY SMES4-93
4.4.2
TESTING OF HYPOTHESES .................................................................. 4-95 4.4.2.1 ORGANISATIONAL LEARNING AND INNOVATION ............................. 4-95 4.4.2.2 ORGANISATIONAL LEARNING AND FIRM PERFORMANCE ................. 4-97 4.4.2.3 INNOVATION AND FIRM PERFORMANCE ........................................... 4-99 4.4.2.4 INDIRECT EFFECTS OF INNOVATION ............................................... 4-100
4.4.3
LEARNING, INNOVATION AND PERFORMANCE BETWEEN FAMILY AND NON-FAMILY SMES ........................................................................ 4-102
4.4.3.1 ORGANISATIONAL LEARNING AND INNOVATION ........................... 4-105 4.4.3.2 ORGANISATIONAL LEARNING AND FIRM PERFORMANCE ............... 4-107 4.4.3.3 INNOVATION AND FIRM PERFORMANCE ......................................... 4-109
4.5 CHAPTER SUMMARY ...........................................................................4-111
5.
DISCUSSION ................................................................................5-112 5.1 INTRODUCTION ...................................................................................5-112 5.2 OVERVIEW OF RESEARCH ...................................................................5-112 5.3 WITHIN FAMILY ANALYSIS AND TEST RESULTS ..................................5-115 5.4 DISCUSSION OF “WITHIN” RESULTS ....................................................5-117 5.4.1
ORGANISATIONAL LEARNING AND INNOVATION.............................. 5-117
5.4.2
ORGANISATIONAL LEARNING AND FIRM PERFORMANCE .................. 5-120
5.4.3
INNOVATION AND FIRM PERFORMANCE ........................................... 5-123
5.4.4
INDIRECT EFFECT OF INNOVATION ................................................... 5-125 vii
5.5 BETWEEN FAMILY AND NON-FAMILY ANALYSIS AND TEST RESULTS .5-126 5.6 DISCUSSION OF “BETWEEN” RESULTS.................................................5-128 5.7 CHAPTER SUMMARY ...........................................................................5-131
6.
CONCLUSION ..............................................................................6-132 6.1 INTRODUCTION ...................................................................................6-132 6.2 CONCLUSIVE FINDINGS .......................................................................6-132 6.3 CONTRIBUTION OF THE RESEARCH .....................................................6-134 6.3.1
CONTRIBUTION TO THEORY............................................................. 6-135
6.3.2
CONTRIBUTION TO RESEARCH ......................................................... 6-137
6.3.3
CONTRIBUTION TO PRACTICE .......................................................... 6-139
6.4 LIMITATIONS OF THE RESEARCH .........................................................6-140 6.5 AVENUES FOR FURTHER RESEARCH ....................................................6-143 6.6 CONCLUDING REMARKS......................................................................6-146
7.
REFERENCES ..............................................................................7-148
8.
APPENDICES ...............................................................................8-170 APPENDIX A
APPROVAL TO ACCESS CURF ON CD-ROM......................8-170
APPENDIX B
SOME PREVIOUS RESEARCH UNDERTAKEN USING BLS DATA .........................................................................8-172
APPENDIX C
THE BLS QUESTIONNAIRE ITEMS USED IN THIS STUDY ....8-178
APPENDIX D
REGRESSION RESULTS USED TO DETERMINE THE INTERVENING EFFECTS .....................................................8-181
APPENDIX E
CHOW TEST RESULTS .........................................................8-182
APPENDIX F
THE MODERATING EFFECTS OF EQUITY CAPITAL ON THE RELATIONSHIP BETWEEN ORGANISATIONAL LEARNING AND INNOVATION ..............................................................8-183
APPENDIX G
THE GENERATIONAL EFFECTS OF ORGANISATIONAL LEARNING ON INNOVATION AND FIRM PERFORMANCE ....8-188
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LIST OF TABLES Table 2-1 Indicators of Growth in Interest in Organisational Learning.................... 2-16 Table 2-2 20 Years of Defining Organisational Learning......................................... 2-17 Table 2-3 Relationship between Organisational Factors and Innovation.................. 2-28 Table 2-5 Authors Who Have Contributed to Defining Family Business ................ 2-37 Table 2-6 SMEs by Number of Employees............................................................... 2-43 Table 2-7 Research Questions and Hypotheses ........................................................ 2-58 Table 3-1 BLS Sample by Year and Panel Status ..................................................... 3-61 Table 4-1 Industry Distribution of Firms .................................................................. 4-80 Table 4-2 Distribution of Firms by Number of Employees – 1997/98 ..................... 4-81 Table 4-3 Distribution of Firms by Age – 1997/98................................................... 4-83 Table 4-4 Distribution of Firms by Legal Status – 1997/98 ..................................... 4-84 Table 4-5 Continuing Firms ...................................................................................... 4-86 Table 4-6 Characteristics of Sampled Firms ............................................................. 4-87 Table 4-7 Incorporated Small and Medium-Sized Manufacturing Firms ................. 4-88 Table 4-8 Family and Non-family SMEs .................................................................. 4-89 Table 4-9 Distribution of Family Firms by Generations ........................................... 4-91 Table 4-10 Summary Descriptive Statistics and Correlation Matrix for the Full Sample (N-222) ........................................................................... 4-92 Table 4-11 Summary Descriptive Statistics and Correlation Matrix for the Family SMEs Sample (N-104)............................................................ 4-94 Table 4-12 Organisational Learning and Innovation in Family SMEs........................ 4-95 Table 4-13 Hypothesis Testing Results: Organisational Learning and Innovation in Family SMEs ............................................................................................ 4-96 Table 4-14 Organisational Learning and Firm Performance in Family SMEs ............ 4-97 Table 4-15 Hypothesis Testing Results: Organisational Learning and Firm Performance in Family SMEs .......................................................... 4-98 Table 4-16 Innovation and Firm Performance in Family SMEs ................................. 4-99 ix
Table 4-17 Hypothesis Testing Results: Innovation and Firm Performance in Family SMEs ...................................................................................... 4-100 Table 4-18 Intervening Effects of innovation between Organisational Learning and Performance in Family SMEs .......................................................... 4-101 Table 4-19 Hypothesis Testing Results: Intervening Effects of Innovation between Organisational Learning and Performance in Family SMEs .... 4-102 Table 4-20 Differences of Means Tests .................................................................... 4-103 Table 4-21 Summary Descriptive Statistics and Correlation Matrix for the Non-Family SMEs Sample (N-118) ................................................. 4-104 Table 4-22 Organisational Learning and Innovation Between Family and Non-family SMEs ................................................................................... 4-105 Table 4-23 Hypothesis Testing Results: Organisational Learning and Innovation: Between Family and Non-family SMEs ................................................. 4-106 Table 4-24 Organisational Learning and Performance Between Family and Non-family SMEs ................................................................................... 4-108 Table 4-25 Hypothesis Testing Results: Organisational Learning and Firm Performance: between Family and Non-family SMEs ........................... 4-109 Table 4-26 Innovation and Firm Performance: Between Family and Non-family SMEs ...................................................................................................... 4-110 Table 4-27 Hypothesis Testing Results: Innovation and firm performance: Between family and Non-family SMEs .................................................. 4-111 Table 5-1 Variables Used for Regression Analysis................................................. 5-116 Table 5-2 Hypothesis Testing Results ..................................................................... 5-116 Table 5-3 Variables Used for Comparison.............................................................. 5-127 Table 5-4 Hypothesis Testing Results..................................................................... 5-127 Table F-1 The Moderating Effects of Equity Capital on the Relationship between Organisational Learning and Innovation .................................. 8-185 Table F-2 The Moderating Effects of Equity Capital on the Relationship between Organisational Learning and Firm Performance..................................... 8-186 Table G-1 Organisational Learning and Innovation in Family SMEs ..................... 8-188 Table G-2 Organisational Learning and Firm Performance in Family SMEs ......... 8-189
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LIST OF FIGURES
Figure 1-1 Structure of the Research.......................................................................... 1-11 Figure 2-1 The Ripple-Effect Model of Organisational Learning, Innovation and Firm Performance .............................................................................. 2-29 Figure 2-2 An Intention-based Approach ................................................................... 2-33 Figure 2-3 Family Universe Bull's Eye ...................................................................... 2-35 Figure 2-4 Familiness and Competitive Advantage ................................................... 2-38 Figure 2-5 Family Business Systems.......................................................................... 2-39 Figure 2-6 Dominance of Family Businesses in Australia ......................................... 2-42 Figure 2-7 Conceptual Framework – Organisational Learning, Innovation and Firm Performance.............................................................................................. 2-46 Figure 2-8 Conceptual Framework with Hypotheses ................................................. 2-57 Figure 4-1 Distribution of Firms by Ownership - 1997/98 ........................................ 4-85 Figure 6-1 Learning, Innovation and Performance: Within Family SMEs .............. 6-133 Figure 6-2 Learning, Innovation and Performance: Between Family and non-family SMEs.................................................................................... 6-134 Figure F-1 Plot of Moderating Effects of Equity Capital ......................................... 8-187
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ABBREVIATIONS
ABS
Australian Bureau of Statistics
ACFB
Australian Centre for Family Business
ANZSIC
Australian and New Zealand Standard Industry Classification
ASX
Australian Securities Exchange
BCA
Business Council of Australia
BLS
Business Longitudinal Survey
CEO
Chief Executive Officer
CURF
Confidential Unit Record File
DIST
Department of Industry Science and Tourism
EBITDA
Earnings Before Interest, Tax, Depreciation and Amortisation
FBA
Family Business Australia
GDP
Gross Domestic Product
GNP
Gross National Product
KBV
Knowledge-based View
KIUS
Knowledge Integration and Utilisation Systems
OECD
Organisation for Economic Co-operation and Development
SMEs
Small and Medium-Sized Enterprises
RBV
Resource-based View
ROTA
Return on Total Assets
VIFs
Variation Inflation Factors
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CHAPTER ONE 1. INTRODUCTION AND OVERVIEW 1.1 BACKGROUND Organisations continuously search for effective strategies to improve their competitiveness. In stable environments, competitiveness has been achieved through effective operational practices such as specialisation of labour and cost control, as proposed in the early management and economics literature (Mourdoukoutas & Papadimitriou, 1998). Recent changes in the competitive environment, particularly the emergence of globalisation (Birdthistle & Fleming, 2005; Bumes, Cooper, & West, 2003; Kalburgi, 1995; Knight, 2000; Salavou, Baltas, & Lioukas, 2004), knowledgebased economies (Birdthistle & Fleming, 2005; Salavou et al., 2004), and advances in information and communication technology (Bumes et al., 2003; Knight, 2000; Salavou et al., 2004), have compelled organisations to continue to seek new strategies because conventional strategies are no longer sufficient to provide a competitive edge (Chirico & Salvato, 2008; Dixon, 1992). Researchers have proposed the notion of organisational learning (De Geus, 1988; Levitt & March, 1988; Senge, 1990; Sinkula, Baker, & Noordewier, 1997; Watkins & Marsick, 1993) as an effective strategy for sustaining and improving firms’ competitiveness and performance, particularly in dynamic business environments (Birdthistle, 2006; Birdthistle & Fleming, 2005; Mavondo, Chimhanzi, & Stewart, 2005; Sadler-Smith, Spicer, & Chaston, 2001). For example, Birdthistle (2006) asserts
Increasing global competition is changing the nature of knowledge necessary for survival in the world of business. Managing change has become a crucial element
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of competitive advantage for it is only by guiding people through change as fast and as painlessly as possible that the business can hope to respond to market pressures before the world moves on. So the ability to learn is a priority for businesses that wish to compete effectively (Birdthistle 2006, p. 6).
Scholars posit that learning is a unique form of an intangible resource (Foss, 1996a; Nonaka, 1994) whereby individuals in an organisation are stimulated to continually accumulate, utilise and share knowledge for individual as well as firm performance (Inkpen, 1996; Nonaka, 1991; Prahalad & Hamel, 1990; Senge, 1990; Watkins & Marsick, 1993). Moreover, many studies (Edmondson & Moingeon, 1998; Nonaka, 1994; Senge, 1990) attest that the new knowledge and skills created through learning improves firms’ competitiveness and performance by enhancing their capabilities including innovativeness (Baker & Sinkula, 1999; Chirico, 2008; Huber, 1998; Kieser & Koch, 2008; Stata, 1989). Hult, Hurley and Knight (2004) and Woodside (2005) highlight that innovativeness is openness to newness and relates to the firm’s capacity to engage in innovation. From this perspective, research indicates that innovation is associated with the notions of generation, acceptance, and implementation of new ideas, processes, products and services (Damanpour, 1991; Drucker, 2002; Tidd, Bessant, & Pavitt, 2001), and is largely shaped by the firm’s learning orientation (Baker & Sinkula, 1999; Calantone, Cavusgil, & Zhao, 2002; Chirico, 2008; Garcia-Morales, Ruiz Moreno, & LiorensMontes, 2006). Academics and practitioners underscore that firms promoting learning have the ability to create an innovative culture that allows them to maintain a competitive position and perform better. In this sense, researchers recognise that the effect of organisational learning on firm performance is likely to be both direct and indirect. 1-2
Support for direct and indirect effects of organisational learning on firm performance has been found in large, widely-held firms by a number of researchers (Baker & Sinkula, 1999; Calantone et al., 2002). Nooteboom (2006), Bates and Khasawneh (2005), Therin (2002), Baker and Sinkula (1999) and Huber (1998) found support for the linkage between organisational learning and innovation while Calantone et al. (2002) found positive relationships among organisational learning, innovation and firm performance in US manufacturing and service industries. In the context of family businesses, Craig and Moores (2006) found that established family firms in Australia placed substantial importance on innovation practices and strategy. Despite the growing interest in organisational learning as an effective strategy for firm performance, no empirical research has explored the links between organisational learning, innovation and performance in family firms1 - the firms which are most prevalent in the business domain in most economies. Family businesses are reckoned as major contributors to the well-being of the economy in terms of employment generation, wealth creation, and industrialisation (Neubauer & Lank, 1998) and are considered the backbone of economies (Bird, Welsch, Astrachan, & Pistrui, 2002). Arguably family firms have a priori features e.g. long tenure CEOs (Le Breton-Miller & Miller, 2006; Moores, 2009; Tsai et al, 2006), higher of levels of trust and interaction (Jones, 1983; Miller, Breton-Miller, & Scholnick, 2008) between management and employees, flexible structures (Birdthistle, 2005) and unique social systems (Zahra, Hayton, Neubaum, Dibrell, & Craig, 2008; Zahra, Neubaum, & Larraneta, 2007)) which suggest
1
The terms “family firm”, “family business” and “family-controlled firm” are used interchangeably in this study.
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they might encourage greater learning than non-family firms. Moreover, taking changes in the competitive, technological, and global environments into account, recent research (Zahra et al., 2008) highlights the importance of studying strategies of family firms associated with innovation and retention of market position. With this background, given a combined interest in organisational learning, innovation, and firm performance, this study examines the direct effects of (a) organisational learning on innovation and firm performance (b) innovation on performance and, (c) the indirect effect of organisational learning on firm performance via innovation in family firms, in particular family small and medium-sized enterprises (SMEs)2. In addition, the effects of organisational learning on innovation and firm performance between family and non-family firms are compared.
1.2 RESEARCH QUESTIONS As mentioned previously, researchers acknowledge that organisational learning is a strategy that has the capacity to generate and advance a firm’s resources for organisational development and adaptation. It has been proposed that organisational learning provides a platform for firms to accumulate, utilise and share knowledge, and that it facilitates innovation, thereby stimulating performance and growth of the firm (Garvin, 1993; Inkpen, 1996; Nonaka, 1991; Senge, 1990; Watkins & Marsick, 1993) in a changing environment.
2
The European Commission (2005) defined the SME as an enterprise which employs fewer than 250 persons and which has an annual turnover not exceeding EUR 50 Mn, and/or an annual balance sheet not exceeding EUR 45 Mn. In Australia, firms employing more than 4 and fewer than 200 people are often defined as SMEs (Ministry of Economic Development (MOED) New Zealand, 2006, p. 35).
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Although few researchers have examined the relationships between organisational learning and the performance of SMEs (Chaston, Badger, & Sadler-Smith, 2001; SadlerSmith et al., 2001), none have examined the relationships between organisational learning, innovation and performance in family SMEs, in either the organisational learning or family business research domains. Only one study (Birdthistle, 2006) has investigated the learning organisation characteristics in Irish family SMEs. While the majority of the research undertaken in family businesses has dealt with management succession, governance (Denison, Lief, & Ward, 2004; Wortman, 1994) and sibling rivalry (Birdthistle & Fleming, 2005), organisational learning research (Birdthistle & Fleming, 2005; Gibb, 1997) has extensively focused on large widely-held firms. Hence there is a need for exploring the organisational learning – innovation – firm performance linkages in family SMEs. With this background, this study addresses the following research questions;
(1)
Does organisational learning in family SMEs affect firm innovation?
(2)
Does organisational learning in family SMEs affect firm performance?
(3) (a) Does innovation in family SMEs affect firm performance? and, (b) Is the relationship between organisational learning and firm performance intervened by innovation? (4)
Do these relationships and patterns in family SMEs differ from those of nonfamily SMEs?
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1.3 OBJECTIVES OF THE RESEARCH As mentioned, the overarching objective of this dissertation is to examine the relationships between organisational learning, innovation and firm performance in family SMEs. The lack of research in this area highlights a knowledge gap. This study aims to address this gap and in doing so provide directions for family firm owners and management to sustain and improve their businesses' performance. With this in mind, the research addresses the following topics in particular: organisational learning, innovation, family businesses and the nexus between organisational learning, innovation and firm performance in family SMEs. The detailed objectives that guide the research are: -
To review and analyse relevant theoretical literature that focuses on organisational learning, innovation and family businesses.
-
To generate a set of empirically testable hypotheses linking organisational learning, innovation and performance “within” family SMEs and “between” family and non-family SMEs.
-
To empirically test the hypotheses. This includes operationalising the theoretical constructs and testing the hypotheses using appropriate quantitative techniques.
-
To explore the relationships between organisational learning, innovation and firm performance “within” family SMEs and “between” family and nonfamily SMEs.
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-
To discuss the empirical and practical contributions of the research findings, to assess the limitations of the study and to present suggestions for future research.
1.4 SCOPE OF THE RESEARCH This research is restricted to manufacturing SMEs in Australia and hence the findings and the conclusions drawn from the research are representative of Australian manufacturing SMEs only.
1.5 CONTRIBUTION OF THE RESEARCH This research can be justified in theoretical and practical terms. The theoretical contribution includes a better understanding of the strategic importance of organisational learning and innovation for family SME performance, an area in which empirically tested studies are scarce. Moreover, the theoretical contribution helps researchers to advance knowledge in the areas of organisational learning and innovation in family businesses. The practical contributions are beneficial to practitioners and the policy-makers who wish to improve firms’ competitiveness and performance.
1.5.1 FAMILY BUSINESS, ORGANISATIONAL LEARNING AND INNOVATION FIELDS
The findings of this research provide a valuable theoretical contribution to the fields of family businesses, organisational learning and innovation. First, this research includes a comprehensive examination of the combination of organisational learning, innovation and performance in family SMEs, which is an under-researched area, by integrating the literature on organisational learning, innovation and family businesses. This integrated
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review of the relevant literature has the potential to be a significant contribution in itself. Second, the research contributes to filling the gap in strategy-focused research in the domain of family businesses. Third, prior studies admit the paucity of empirical research into the area of family businesses and emphasise the need for more research (Shanker & Astrachan, 1996; Sharma, Chrisman, & Chua, 1997), specially in the field of organisational adaptation and changes (Chirico & Salvato, 2008; Hatum & Pettigrew, 2004). In responding to that need this research is designed to empirically test organisational learning, innovation and performance in family firms in particular family SMEs. Furthermore, the research compares and contrasts the effects of organisational learning on innovation and firm performance, and the effects of innovation on performance of family SMEs with non-family SMEs, contributing to the resource-based view (RBV) inspired debate about the “familiness” basis for sustainable competitive advantages in family firms.
1.5.2 PRACTITIONERS AND POLICY-MAKERS The findings of this research provide a practical contribution to practitioners and policymakers. As discussed, a major issue of family firms, particularly family SMEs, is maintaining long-term survival and success in a competitive environment (Zahra et al., 2008). If the findings of this research support the proposition that organisational learning improves the innovation and the firm performance of family SMEs, then practitioners of family businesses can use those findings to strengthen the competitive position of their firms. Moreover, at state and national levels, policy-makers can make use of the findings when formulating policies and programs for supporting and developing SMEs.
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1.5.3 RESEARCHERS This research provides avenues for subsequent researchers to cross-check and validate the findings in countries outside Australia. Further, researchers can use this framework to examine the relationships between organisational learning, innovation and firm performance in industrial sectors other than manufacturing, and can also extend it to large companies, not-for-profit organisations and to government-controlled institutions.
1.6 RESEARCH METHOD Research method occupies a central position in the research process. In this research a quantitative method is adopted. This method allows the researcher to use statistical models and hypothesis testing (Hughes, 1990). Drawing from the literature, a conceptual framework is developed and empirically tested with secondary data to address the research questions. The data used for the analysis are obtained from the Business Longitudinal Survey (BLS) conducted by the Australian Bureau of Statistics (ABS) over the financial years 1994/95, 1995/96, 1996/97 and 1997/98, which is the most recently available comprehensive longitudinal database in Australia. In the BLS, data were collected using self-administered, structured questionnaires predominantly containing closed questions. The corpus contains data on 9,732 business units that employed fewer than 200 persons, and is a broad representation of Australian SMEs. The hypotheses are empirically tested using multiple regression methods and Chow tests. The direct links between organisational learning, innovation and firm performance are tested using linear regression analysis. The indirect link that is the intervening effect of innovation between organisational learning and firm performance is tested using 1-9
linear regression analysis proposed by Baron and Kenny (1986) and Frazier, Tim and Barron (2004). The Chow test is used to compare the effects of organisational learning, innovation and performance between family and non-family SMEs. In addition, descriptive statistics are employed to analyse and interpret the statistical attributes of the sample and variables.
1.7 STRUCTURE OF THE RESEARCH This research is developed through six chapters providing details of the background to the research, research questions, research objectives, scope of the research, research, research method, structure of the research, theoretical background of the research, conceptual framework, research variables and their measurement techniques, data source, analysis and interpretation of findings, discussion and conclusion including research contribution, limitations of the research and avenues for future research. Following this introduction, Chapter Two reviews the literature related to the topic with a specific focus on the major concepts that impact on this research. The literature is drawn from three major streams – organisational learning, organisational innovation, and family businesses – which provide the theoretical framework within which this study fits and the platform upon which the research questions are developed. An overview of Australian family businesses and the hypotheses of the research are also presented. Chapter Three outlines the research method including data collection (the BLS), sample selection, operationalisation of conceptual framework, and statistical techniques used. The results of statistical analyses are reported and interpreted in Chapter Four. Chapter Five presents the research summary and the discussion of the results. Finally, Chapter Six provides the conclusion to the research, including
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conclusive findings, contribution, limitations and avenues for future research. References and appendices follow. A diagram illustrating the research structure is presented in Figure 1.1. FIGURE 1-1
STRUCTURE OF THE RESEARCH
1.8 CHAPTER SUMMARY This chapter provided an introduction and overview of the dissertation. The research questions, research objectives, scope of the research, contribution of the research, research method, and research structure were presented. The background to the dissertation showed the significance of organisational learning in the face of a competitive environment and its strategic importance for firm innovation and performance. Moreover, the chapter highlighted the lack of strategy focused-research in the domain of family businesses and placed emphasis on the need for more research in the area particularly, organisational adaptation and changes. 1-11
CHAPTER TWO 2. LITERATURE REVIEW 2.1 INTRODUCTION This chapter reviews relevant literature to develop a framework for research and hypotheses to address the research questions posed in Chapter One. The chapter consists of six sections including this introduction. Because the area of enquiry of this research is cross-disciplinary the literature from each area is initially considered independently, prior to developing a conceptual framework for the research. Accordingly, Section 2.2 discusses organisational learning and its importance for innovation and firm performance from a strategic perspective. In Section 2.3 organisational innovation is discussed. Section 2.4 discusses the context of this research: family business. Specifically, the significance of family businesses and family business research, the distinctive nature of family firms, and family business in Australia are discussed. The emergent conceptual framework and hypotheses are presented in Section 2.5, and Section 2.6 presents the chapter summary.
2.2 ORGANISATIONAL LEARNING
2.2.1 INTRODUCTION In the global marketplace, maintaining a competitive position is vital for firm survival and success, but it is a challenging task largely due to increasing levels of competition resulting from globalisation (Birdthistle & Fleming, 2005; Bumes et al., 2003; Kalburgi, 1995; Salavou et al., 2004), knowledge-based economies (Birdthistle, 2006; Birdthistle & Fleming, 2005; Salavou et al., 2004), and information and communication technology diffusion (Bumes et al., 2003; Salavou et al., 2004). Strategic management researchers 2-12
(Barney, 2007; Porter, 1980; Teece, Pisano, & Shuen, 1997) emphasise the necessity of adopting an effective strategy in sustaining and safeguarding the firm’s competitive position in such an environment as conventional strategies are no longer sufficient in improving a firm’s competitiveness. In similar fashion, Nonaka (1991, p.96) stressed that in an economy where the only certainty is uncertainty, the one sure source of lasting competitive advantage is knowledge. Successful companies are those that consistently create new knowledge, disseminate it widely throughout the organisation, and quickly embody it in new technologies and products. Taking changes in the environments into account, Chirico and Salvato (2008) highlighted that the speed of change in competitive environments has driven firms to develop processes directed toward changing and increasing their strategic capabilities and adaptiveness for yielding better performance. In this context, researchers suggest that a climate that stimulates learning in a firm has the capacity to create new knowledge and skills. Subsequently, such knowledge and skills enable the firm to be adaptive and innovative (Calantone et al., 2002; Huber, 1998; Hurley & Hult, 1998; Therin, 2002), thereby improving its competitiveness and performance (Craig & Moores, 2006; Nonaka, 1991). Thus, organisational learning has received considerable attention among academics and practitioners as an effective strategy for maintaining firm performance, particularly in the face of turbulent and highly competitive market conditions (Bumes et al., 2003; Sadler-Smith et al., 2001; Sinkula et al., 1997). Further, in a nutshell, a number of reasons can be suggested as to why the study of organisational learning is currently so important. First, the notion of the importance of learning is gaining currency among organisations as they attempt to develop structures and systems that are more adaptable and responsive to change (Dodgson, 1993; Senge,
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1990). Second, ongoing rapid technological change is having a profound influence on organisations (Dodgson, 1993; Therin, 2002). The turbulence engendered by technological changes in products, services, processes and organisation increases the uncertainties facing firms (Salavou et al., 2004). Third, it is argued that in a competitive environment learning is a crucial element of firm strategy in creating new knowledge and skills which are strategic resources that provides competitive advantage (Foss, 1996a,b; Garvin, 1993; Nonaka, 1991; Senge, 1990), and should be a focus of management concern. This view is closely related to the knowledge-based view (KBV) of the firm, in that the firm is seen as a bundle of competencies and capabilities that can be used to create competitive advantage (Grant & Spender 1996; Grant, 1996b). So, organisational learning is a topic that has taken on increased importance as scholars attempt to understand how organisations are able to continually adapt to their environments (Waldman, Keller, & Berson, 2006). Given its importance, the notion of organisational learning has been extensively discussed in a broad range of literature, and it has been shown that if an organisation implements learning strategies effectively and regularly, it is certain to enhance firm performance (Bell, Whitwell, & Lukas, 2002; Chirico, 2008; Garcia-Morales et al., 2006; Garvin, 1993; Senge, 1990; Stata, 1989) in both a direct and an indirect manner. 2.2.2 CONCEPT OF ORGANISATIONAL LEARNING Conceptions of organisational learning are ubiquitous. The topic has been studied for many years (Argyris & Schon, 1978; Fiol & Lyles, 1985; Garvin, 1993; Huber, 1991; Levitt & March, 1988; Senge, 1990;1996; Stata, 1989) in a range of academic disciplines including psychology, organisational development, management science, sociology and organisation theory, strategy, production management, leadership and 2-14
cultural anthropology (Easterby-Smith, 1997; Mavondo et al., 2005). Whereas psychologists examine organisational learning in terms of how individual learning occurs via human cognitive processes (Dodgson, 1993), theorists in organisational development view organisational learning from an organisational structural perspective, in which they explore how learning is developed within organisations (Kim, 1993; Levitt & March, 1988). The management science perspective is concerned with the gathering and processing of information in and about organisations (Easterby-Smith, 1997). The social and organisational theory perspective focuses on the broader social systems and organisational structures where learning may be embedded, and which may affect organisational learning, whereas the production management perspective focuses primarily on the relationship between learning and organisational productivity and efficiency. Leadership perspectives focus on identification, nurturing and utilisation of employees’ knowledge and skills in the most effective way to meet the challenges of the organisational environment. Finally, cultural anthropologists see culture, both in its organisational and national manifestations, as a significant cause and effect of organisational learning. The strategy perspective adopted in this study views organisational learning as a purposive quest to retain and to improve competitiveness, productivity and innovativeness in uncertain technological, market and environmental circumstances (Baker & Sinkula, 1999; Chirico, 2008; Garvin, 1993; Jones & Hendy, 1994; Senge, 1990; Therin, 2002). Strategy scholars (Garvin, 1993; Grant & Spender 1996; Nonaka, 1991; Senge, 1990) assert that learning is a strategic resource which provides a firm with a competitive advantage in the form of knowledge and skills. In similar vein, highlighting the significance of learning, studies (De Geus, 1988; Dixon, 1999) suggest that the ability and the rate at which organisations can learn to react more quickly than 2-15
their rivals create for them a source of competitive advantage and consequently, improve their capabilities and performance. Although interest in organisational learning has grown increasingly during the last three decades (Bumes et al., 2003; Crossan & Guatto, 1996; Huber, 1991) (see Table 2.1), owing to emerging appreciation of its relevance to organisation competitiveness (Baker & Sinkula, 1999; Dodgson, 1993; Garvin, 1993; Huber, 1991; Senge, 1990), the notion of organisational learning has also been criticised on the grounds of the lack of definitional convergence across business disciplines and the insufficient conceptual rigour. TABLE 2-1 INDICATORS OF GROWTH IN INTEREST IN ORGANISATIONAL LEARNING
Number of organisational learning articles written Number of journals publishing
1960s
1970s
1980s
1990s
2000s*
3
19
50
184
317
3
18
35
80
153
3
15
44
149
302
organisational learning articles Number of authors or groups of authors writing organisational learning articles
Extended from: Crossan & Guatto (1996), p. 108 * Data gathered by researcher through a comprehensive search of on-line databases The lack of definitional convergence is demonstrated by the numerous definitions shown in Table 2.2. However, Crossan, Lane and White (1999) and Huber (1991) have suggested that the definitional confusion is perhaps partly attributable to the diversity of research domains in which learning phenomena have been explored and to the different ontological stances of researchers.
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TABLE 2-2 20 YEARS OF DEFINING ORGANISATIONAL LEARNING Author (s)
Definitions A process by which members of an organisation detect error or anomaly and correct it by restructuring organisational
Argyris & Schon (1978, p. 2)
theory of action (the norms, assumptions, and strategies inherent in collective practices) and by encoding and embedding the results in their inquiry in organisational maps and images.
The process whereby management teams change their De Geus (1988, p. 71))
shared mental models of their company, markets and competitors. Organisational learning occurs through shared insights,
Stata (1989, p. 64)
knowledge and mental models … and builds on past knowledge and experience. An organisation skilled at creating, acquiring, and
Garvin (1993, p. 80)
transferring of knowledge and at modifying its behaviour to reflect new knowledge and insights. The way firms build, supplement and organise knowledge and
Dodgson (1993, p. 337)
routines around their activities and within their culture, and the way they adopt and develop organisational efficiency by improving the broad skills of their work force.
A process by which organisations change their cultures Loizo (1995, p. 25)
and systems in relation to market conditions; and they must do this in order to improve their competitiveness and achieve a sustainable competitive advantage. The acquisition of new knowledge by actors who are able and
Miller (1996, p. 486)
willing to apply that knowledge in making decisions or influencing others in the organisation.
A process in which an organisation’s members actively Edmondson & Moingeon (1998, p.
use data to guide behaviour in such a way as to promote
12)
ongoing adaptation of the organisation.
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For this study, the definition put forward by Templeton, Lewis and Snyder (2002) is used, as it contains a synthesis of 78 explicit definitions of organisational learning. They defined organisational learning as a set of actions (knowledge acquisition, information distribution, information interpretation and organisational memory) in the organisation that intentionally and unintentionally influence positive organisational change. Moreover, the phenomenon of organisational learning can be viewed from the viewpoint of different learning systems. By analysing organisational learning systems in a number of business organisations, Shrivastava (1983) identified six different types of learning system: (1) One man (sic) institutions: this is an organisational learning situation in which one person is the key to all learning processes, e.g. the entrepreneur and the chief executive officer. (2) Mythological learning systems: this system considers organisational myths, corporate stories and the corporate culture are as knowledge base. Myths lay the groundwork for development of organisational norms of knowledge sharing. (3) Information seeking culture: this term describes a system in which organisationally relevant information is shared among organisational members on a routine basis through networks and communication. Furthermore in this system, organisational members are encouraged to continuously seek and acquire information which may be directly or indirectly relevant to their individual tasks. (4) Formal management systems: these are the established systematic procedures developed to guide many of the standard and non-standard organisational activities in organisations, such as strategic planning, management information systems, environmental scanning, financial and budgetary control systems that facilitate learning. (5) Participative learning systems: this includes ad-hoc teams, quality circles and trouble-shooting teams that create learning in organisations through interactions. (6) Bureaucratic learning
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systems: this includes an elaborate system of procedures and regulations that give exact advice for specific situations. Although various researchers have propounded different views as to what constitutes organisational learning (Shrivastava, 1983), closer inspection of this notion reveals that organisational learning is a process that creates new knowledge and skills for individuals. Researchers (Kim, 1993; Nonaka, 1991; Senge, 1990) have posited that an organisation learns through its individual members and consequently that organisational learning is shaped by individual learning. Thus, it is emphasised that the more individuals learn, the more likely are their organisations to attain success. However, researchers share the basic assumption that organisational learning is more than the sum of all individual learning activities and that it is cumulative (Argyris, 1993).
2.2.3 ELEMENTS OF ORGANISATIONAL LEARNING Several researchers (Nevis, DiBella, & Gould, 1995; Shrivastava, 1983; Templeton et al., 2002) have identified a variety of elements in organisational learning. However, synthesising the literature, Huber (1991) and Templeton et al. (2002) proposed four inter-related elements of organisational learning: knowledge acquisition, information dissemination, information interpretation and organisational memory. Knowledge acquisition is the process by which knowledge is obtained. The knowledge/information may be obtained from a vast range of sources including customer surveys, research and development activities, performance reviews, scanning the organisational environment, analysing competitors’ products, internal and external networks (Huber, 1991; Nevis et al., 1995) and employee training and development programs (Garvin, 1993; Habbershon & Williams, 1999; Paul, 1994). Thus, with the knowledge acquired, there is a potential
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for organisations to learn how to improve and innovate their products/services and processes, leading to competitive advantage. Second, information dissemination is a process by which information from different sources is shared, leading to new information or understandings (Huber, 1991). In this process, information is distributed through the organisation which actually facilitates knowledge sharing among the employees. Some examples of knowledge sharing include staff development (Goh, 1998), environmental scanning (Habbershon & Williams, 1999; Shrivastava, 1983; Wang, 2008), strategic planning, networking and communication (Habbershon & Williams, 1999; Shrivastava, 1983). In addition, information dissemination provides an opportunity for organisations to learn from the experience of others (Argote & Ingram, 2000). A growing body of literature indicates that organisations which are proficient at knowledge transfer are more likely to be more productive and innovative than those which are less adept (Argote, Ingram, Levine, & Moreland, 2000; Darr, Argote, & Epple, 1995). Third, information interpretation is a process by which distributed information is given one or more commonly understood interpretations (Huber, 1991). This process involves organisational members conceptualising the information that is distributed. Information interpretation is synonymous with Senge’s (1990) construct of building a shared vision, where a firm’s vision is to be shared with every organisational member so that the organisation can learn. Finally, organisational memory is a means by which knowledge is stored for future use. Organisational memory is important to learning because without memory learning
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would have a short life due to employee turnover and the passage of time (Huber, 1991; Levitt & March, 1988). Our review of extant literature on organisational learning with special reference to knowledge
acquisition,
interpretation
and
dissemination
of
information
and
organisational memory has resulted in recognising three major sources of creating, accumulating and sharing knowledge in organisations: commitment to learning, shared vision and networking. They are used in operationalising the organizational learning construct in this study. Overall, researchers consider that organisational learning creates new knowledge and skills which are key strategic resources (De Geus, 1988; Grant, 1996b; Nonaka, 1991), which have the capacity to enhance firms’ innovations (Baker & Sinkula, 1999; Bates & Khasawneh, 2005; Calantone et al., 2002; Chirico, 2008; Huber, 1998; Hurley & Hult, 1998) and performance (Baker & Sinkula, 1999; Calantone et al. 2002; Craig & Dibrell, 2006; Damanpour & Evan, 1984, Rothwell, 1992). Moreover, it has been acknowledged that organisational learning depends on practices and routines, patterns of interaction both within and outside the firm, and the ability to mobilise individual tacit knowledge and promote interaction. Such learning can be encouraged through careful design of practices, routines and relationships, or through a more flexible, fluid organisation in which individuals are encouraged to develop new ideas and ways of doing things. Researchers argue that learning means integrating new knowledge or mixing existing knowledge in different ways, and then learning leads to newness and thus to innovation. They highlight the convergence between knowledge and innovativeness, suggesting that organisational learning may be a close relative of organisational innovation (Hurley & Hult, 1998; Kieser & Koch, 2008). That is, higher levels of innovation are associated 2-21
with cultures that promote learning. Moreover, some suggest that innovation is a byproduct of organisational learning (Daryl, 1992; Therin, 2002).
2.3 ORGANISATIONAL INNOVATION 2.3.1 OVERVIEW OF INNOVATION Academic interest in innovation has been apparent since 1928 with Schumpeter’s seminal work on the instability of capitalism, which underlined innovation as the driving force of capitalism. Since then subsequent authors (Abernathy & Clarke, 1985; Damanpour, 1991; Tidd et al., 2001) have used the context of economic entities to explore the concept of innovation, and have supported the proposition that innovation has a direct impact on firm performance. Overall, innovation provides organisations with a means of adapting to the changing environment (Greve, 2007; Thompson, 1965), and is often critical for firm longevity and success. The field of innovation is broad, complex and subject to different interpretations within its different strands (Damanpour, 1991; Wolfe, 1994). The organisational design literature focuses predominantly on the link between structural forms and the propensity of an organisation to innovate (Kimberly & Evanisko, 1981; Mintzberg, 1979). In this strand the unit of analysis is the organisation, and the researcher’s main purpose is to identify and explore the structural characteristics that impact on organisational innovation. Scholars of organisational learning (Argyris & Schon, 1978; Baker & Sinkula, 1999; Hurley & Hult, 1998; Nonaka, 1994), on the other hand, tend to focus on how organisations develop new ideas for problem solving and organisational renewal. They consider that organisational innovation is associated with the learning and organisational knowledge creation processes. Research centres on organisational change
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and adaptation, and the significance of creating new organisational forms to enhance the innovativeness of the organisation (Hannan & Freeman, 1984). Craig and Moores (2006) underlined that capability in innovation management develops over time and must involve a process of continual learning. Researchers view innovation as a dynamic process in which knowledge and skills are accumulated through learning and interaction. The present study also embodies the view that learning has an impact on organisational innovation in family SMEs and consequently, generates better firm performance.
2.3.2 DEFINING INNOVATION 2.3.2.1 THEORETICAL DEFINITIONS The term innovation comes from the Latin innovare, meaning “to make something new” (Tidd et al., 2001). Indeed, the idea of newness is included in some form in all definitions of innovation. For example, Thompson (1965) defined innovation as the generation, acceptance, and implementation of new ideas, processes, products or services. Damanpour (1991) defined innovation as the generation, development, and implementation of new ideas or behaviours which can be a new product or service, a new production process, a new structure or administrative system, or a new program pertaining to organisational members. Rogers (1998) defined innovation as the application of new ideas to the product, process or any other aspect of a firm’s activities. According to Drucker (2002), innovation is a specific function of entrepreneurship, the means by which the entrepreneur either creates new wealth-producing resources or endows existing resources with enhanced potential for creating wealth. Dibrell, Davis and Craig (2008) underlined that innovations vary in complexity and can range from
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minor changes to existing products, processes, or services to breakthrough products, and to processes or services that introduce first-time features or exceptional performance. Overall, these definitions underscore that innovation can come in a variety of forms such as products, services, and processes, with a face of newness and/or improvement. However, the use of terms such as “new” or “improved” retains a degree of subjectivity in the notion of innovation. What is new to one firm is not necessarily new to another; therefore it is possible that the innovation in two different firms is not identical. This observation emphasises the degree of complexity associated with the term. 2.3.2.2 TECHNICAL DEFINITIONS Besides the theoretical definitions, examination of the technical definitions of innovation helps us understand how different institutions interpret the concept of innovation for their policy-making and administrative purposes. In this context, the definition of innovation put forwarded by the Organisation for Economic Co-operation and Development (OECD) is widely used in measuring and interpreting the innovative initiatives, particularly in the OECD countries. The OECD (2005) defined innovation as the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organisational method in business practices, workplace organisation or external relations. The OCED identified four types of innovation: product, process, marketing and organisational. Product innovation is the introduction of a good or service that is new or significantly improved with respect to its characteristics or intended uses. This includes significant improvements in technical specifications, components and materials, incorporated software, user-friendliness or other functional characteristics. Process innovation refers to the implementation of a new or significantly improved production or delivery method. This includes significant 2-24
changes in techniques, equipment and/or software. Process innovation can be intended to decrease unit costs of production or delivery, to increase quality, or to produce or deliver new or significantly improved products. Marketing innovation relates to the implementation of a new marketing method involving significant changes in product design or packaging, product placement, product promotion or pricing. Finally, organisational innovation concerns the implementation of a new organisational method in the firm’s business practices, workplace organisation or external relations. As the current study is undertaken in the Australian context, clarification of its definitions is worthwhile. The Australian Bureau of Statistics (ABS), Australia's national statistical agency, defines innovation as the process of introducing new or significantly improved goods or services and/or implementing new or significantly improved processes. New goods or services or processes may involve the development of new technology, an adaptation of existing technology to a new use, or it may be nontechnological in nature (ABS, 2006). One of the key strengths of this definition is its close connection to the OECD definition of innovation. The Department of Industry, Science and Tourism (DIST) uses a relatively broad definition of innovation: at the level of an individual firm, innovation might be defined as the application of ideas that are new to the firm, whether the new ideas are embodied in products, processes or services or in work organisation, management or marketing. The Business Council of Australia (BCA), an association of the CEOs of Australia’s leading corporations, defines innovation as creating or doing new things or doing things in new or better ways, drawing on knowledge, creativity and collaboration to add value to products, services and processes (BCA, 2007, p.34). Although different institutions have defined
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innovation in different ways, a common thread in these definitions is the idea of creating new/improved products, services or processes. As data for this research is obtained from the Business Longitudinal Survey (BLS) conducted by the ABS, its definition of innovation is used in this research in operationalising the conceptualised framework.
2.3.3 TYPES OF INNOVATION Researchers have argued that distinguishing types of innovation is necessary for understanding organisations’ adaptation behaviour and identifying the determinants of innovation (Damanpour, 1991). In early literature in the field, Schumpeter (1934) outlined five categories of innovation: (a) introduction of a new product or an improvement to an existing product, (b) introduction of a new process or an improvement to an existing process, (c) opening of a new market, (d) development of new sources of supply for raw materials or other inputs, and (e) changes in industrial organisation both inter-organisational and intra-organisational, such as the creation of a monopoly firm or a change in management structure. The categorisation of innovation proposed by Abernathy and Clark (1985) has four different modes: (a) architectural – new technology that departs from established systems of production, and in turn opens up new linkages to markets and users, characteristic of the creation of new industries as well as the reformation of old ones, (b) niche – opening new market opportunities through the use of existing technology, (c) regular – involving change that builds on established technical and production competence and is applied to existing markets and customers, and (d) revolutionary – innovations which tend to disrupt or make obsolete existing paradigms or technologies in an industry.
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On the basis of the perceived extent of change created by innovation, Tidd et al. (2001) identified three types of innovation: (a) transformational - when an organisation does something fundamentally different, applying revolutionary new technology or processes to change the organisation, (b) radical - transforming the relationship between customers and suppliers, restructuring marketplace economics, displacing current products and creating entirely new product categories (Salomo, Gemunden, & Leifer, 2007), and (c) incremental - when standard technology is applied in new ways, as in process improvements, or when best-of-breed technologies are used in innovative ways, bringing better products or services by listening to customers. In Schumpeter’s view, radical innovation creates major disruptive changes, whereas incremental innovation continuously advances the process of change. The matrix of change presented by Tidd et al. is based on two forms: the first is the “things”, the products or services which an organisation offers, and the second is the change in the way in which the product is created and delivered; that is, the process. Damanpour (1991) maintains that among numerous typologies of innovation advanced in the literature, three have gained most attention: (a) administrative and technical, (b) product and process, and (c) radical and incremental. Whereas an administrative innovation relates to management oriented processes such as structure, human resource management, and accounting systems, a technical (or technological) innovation is directly related to the production of a product using new or upgraded technology. Product innovations are outputs of the organisation. A process innovation assists the organisation to produce products or services (outputs) from inputs. On a continuum, innovation can be described as incremental to radical, according to the degree of change required to implement the innovation.
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2.3.4 FACTORS INFLUENCING INNOVATION Innovation in an organisation is influenced by many factors (Damanpour, 1991) including both environmental and organisational (Kim, 1980; Kimberly & Evanisko, 1981; Mohr, 1969). Kimberly and Evanisko maintain that an environmental factor such as industry type has a significant effect on innovation. Studies report contradictory results relating to firm size and innovation. Some researchers (Cohen & Kleppler, 1996a; Kimberly & Evanisko, 1981) have found that organisation size positively affects innovation, yet others (Holmstrom, 1989; Martinez-Ros & Labeaga, 2002) have reported no significant relationship between firm size and innovation. Several researchers (Ahmed, 1998; Bhattacharya & Bloch, 2004; Damanpour, 1991; Laursen & Foss, 2003) have identified a number of organisational factors that correlate with the adoption of innovation. Table 2.3 presents reported relationships between organisational factors and innovation. TABLE 2-3 RELATIONSHIP BETWEEN ORGANISATIONAL FACTORS AND INNOVATION Independent Variables
Relationship
Author(s)
Specialisation, functional differentiation, professionalism, managerial attitudes toward change, technical knowledge resources, administrative intensity, slack
Positive
Damanpour (1991)
resources, external and internal communication Formalisation
Negative
Research and development
Positive
Bhattacharya & Bloch (2004)
Positive
Baker & Sinkula (1999), Calantone et al. (2002), Huber (1998), Hurley & Hutt (1998), Mavondo et al.(2005), Therin (2002)
Organisational learning
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Researchers (Baker & Sinkula, 1999; Calantone et al., 2002; Huber, 1998; Hurley & Hult, 1998; Therin, 2002) have also investigated the relationship between organisational learning and innovation and have shown that organisational learning is positively related to innovation. This relationship is vital since organisational learning can ultimately result in better performance via innovation, as demonstrated by Olsen, Lee and Hodgkinson (2006) (see Figure 2.1). FIGURE 2-1
THE RIPPLE-EFFECT MODEL OF ORGANISATIONAL LEARNING, INNOVATION AND FIRM PERFORMANCE
Source: Olsen et al. (2006), p. 17
We next discuss the context in which this study is based, the family business.
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2.4 FAMILY BUSINESSES 2.4.1 SIGNIFICANCE OF FAMILY BUSINESSES AND FAMILY BUSINESS RESEARCH
Family businesses are the oldest form of business in the world. The study of family businesses has attracted significant attention in recent times because of their prevalence in the global economic and business landscape (Daily & Dollinger, 1993; Gomez-Mejia, Haynes, Nunez-Nickel, Jacobson, & Moyano-Fuentes, 2007; Moores & Mula, 2000; Morck & Yeung, 2004a). Their role in the economy is significant in terms of employment generation, wealth creation and industrialisation. Studies (Miller, Steier, & Le Breton-Miller, 2003) suggest, based on conservative estimates, that more than 75% of all businesses in most economies are family owned. Table 2.4 shows the estimated percentages of family businesses in selected countries. TABLE 2-4 FAMILY BUSINESS IN SELECTED COUNTRIES Country /Region
% of Family Businesses Among All Businesses
North America
US 95% and Mexico 80% (Bournheim, 2000, as cited in the EMCC3 2002, p. 8).
Europe
3
UK 75%, Portugal 70%, Spain 80%, Switzerland 85%, Sweden more than 90%, Italy more than 95% (Neubauer & Lank, 1998, p. 10), Netherlands 52% and Austria 80% (Bournheim, 2000, as cited in the EMCC, 2002, p. 8).
Middle-East
More than 90% (Neubauer & Lank, 1998, p. 10).
East Asia
More than 50% (Tsai, Hung, Kuo, & Kuo, 2006).
European Monitoring Centre on Change.
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Moreover, Carlson, Upton and Seaman (2006) reported that 60% of all employment, 78% of all new jobs, more than 50% of GDP and about 65% of all wages paid in the US were from family firms. The PricewaterhouseCoopers family business survey 2007/08 revealed that family businesses contributed up to 65% of the GNP of EU member states, up to 45% of the GNP of North America, up to 70% of the GNP of Latin America and up to 82 % of the GNP of Asia (PwC, 2008). Similar contribution prevails in Australia (see Section 2.4.4). In spite of the fact that family firms have been in existence and operating for thousands of years as the backbone of economies, it was not until the 1990s that the field was recognised as a separate discipline for scholarly inquiry (Bird et al., 2002). As a result the literature on family firms is not as voluminous as in other areas of management and, more importantly, there are many potential areas for academic scrutiny (Neubauer & Lank, 1998; Ramona, Hoy, Poutziouris, & Steier, 2008). Some have argued (Birdthistle & Fleming, 2005; Wortman, 1994) that because of the lack of a unified paradigm and the concentration on a small segment of the field such as succession, governance and sibling rivalry, research on family firms has not progressed as rapidly or systematically as it could have. However, after the 1990s, researchers began to recognise family business as a separate discipline and began to build a body of knowledge that expanded understanding of this domain. A significant landmark in family business research was the launching of Family Business Review in 1988, a scholarly journal targeting a multidisciplinary audience, by the Family Firm Institute, USA. This breakthrough encouraged more academics to conduct research and publish new knowledge for the field (Sharma, Hoy, Astrachan, & Koiranen, 2007). Although in recent years researchers have made a notable contribution 2-31
to establishing a body of knowledge in the family business domain through systematic and rigorous research, the field is still young and emergent, and much remains to be done (Chrisman, Chua, & Sharma, 2005; Sharma, 2004).
2.4.2 DEFINING FAMILY FIRMS Although it is recognised that family firms are different from non-family firms (Daily & Dollinger, 1993; Sharma et al., 1997), there is no common agreement in the literature as to what the term family firm actually means. Researchers have used different criteria in defining a family firm (Martos, 2007; Wortman, 1994). For example, Robert and Brockhaus (1994) suggested that a family firm is any in which more than one member of the family is affected by business decisions. Daily and Dollinger (1993) attempted to define family firms in terms of firm size, considering family firms as synonymous with small firms. The scanning of family firms’ biographies shows that they range from small corner shops to multinational family-controlled enterprises (Birley, Dennis, & Godfrey, 1999). However, overall most family firms fall into the category of small and medium sized firms (SMEs) (Voordeckers, Van Gils, & Heuvel, 2007). Recognising the diversity of family firms, Handler (1989) highlighted the importance of concentrating on the range of family business configurations when defining the family firm. Litz (1995) identified two main approaches to defining family firms, a structure-based approach (intra-organisational family-based relatedness) and an intention-based approach. Whereas the structure-based approach considers family firms in terms of firm ownership and management, the intention-based approach focuses on the realised and unrealised value preferences of the organisation’s upper echelons and family members. Litz remarked that an obvious shortcoming of the structure-based approach is its inability to appreciate intra-organisational preferences toward family-based relatedness. 2-32
Integrating these two approaches, Litz proposed that a business firm may be considered a family business to the extent that its ownership and management are concentrated within a family unit, and to the extent that its members strive to achieve and/or maintain intra-organisational family-based relatedness (see Figure 2.2).
FIGURE 2-2 AN INTENTION-BASED APPROACH
Source: Litz (1995), p. 77
The components-of-involvement and essence approaches proposed by Chrisman et al. (2005) for defining a family firm can be viewed as a further extension to the Litz approaches. However, in their components-of-involvement approach, Chrisman et al. view family involvement as a sufficient condition for defining family firms. On the other hand, in the essence approach they suggest that mere family involvement is not enough to consider a firm as a family firm; family involvement needs to be directed toward behaviour that produces a certain distinctiveness for a firm to be considered a family firm. It seems that approaches based on family involvement are more favoured by researchers than behavioural approaches (e.g. intention-based and essence approaches) in defining a family firm, as they are easier to operationalise (Chua, Chrisman, & 2-33
Sharma, 1999). However, a behaviourally based approach is essential to study the phenomena of family businesses and to understand why and how they differ from other types of business. In this light, both approaches are equally important to researchers in expanding the body of knowledge in the field of family business. Addressing the definitional debate, Shanker and Astrachan (1996) were among the first to suggest that family firms fall on a continuum rather than belonging to a dichotomous category. They argued that family firms could be categorised according to the degree of family involvement: little direct family involvement, some family involvement and a lot of family involvement, and these degrees of involvement could be used to group family firm definitions as broad, middle and narrow. The broad definition indicates little direct family involvement, where the family has some degree of effective control over strategic direction, and the business is at least intended to remain in the family. This definition implies that the family is not involved in the day-to-day operations of the business but influences decision making, perhaps through board membership and/or significant stock ownership. The middle definition indicates some family involvement; it includes all the criteria in the broad group in addition to requiring a family member(s) to be directly involved in the day-to-day operations of the business and requiring that the founder, or a descendant, runs the business. The narrow definition indicates high family involvement. It includes all the criteria for the middle definition but also requires that multiple generations are involved in the business, there is direct family involvement in day-to-day operations, and more than one family member has significant management responsibility (Shanker & Astrachan, 1996). Figure 2.3 presents the family business definitions by degree of family involvement as put forward by Shanker and Astrachan. 2-34
FIGURE 2-3 FAMILY UNIVERSE BULL'S EYE
Source: Shanker & Astrachan (1996), p. 109. Applying seven different definitions to 427 unquoted companies in the UK, Westhead and Cowling (1998) found that family business statistics were highly sensitive to the definitions employed. According to their least restrictive definition – the company is perceived by the chief executive officer (CEO), managing director (MD) or chairman to be a family business – 78.5% of 427 firms they studied were classified as family firms. However, according to their most restrictive definition – more than 50% of ordinary voting shares are owned by members of the largest single family group related by blood or marriage, the company is perceived by the CEO, MD or chairman to be a family business, 51% or more of the management team are drawn from the largest family group who owns the company, and the company is owned by second generation or more family members – only 15% of the firms were family firms. These findings show that differences in research results for family firms may be attributable to demographic
2-35
sample differences rather than real differences. Thus, Shanker and Astrachan (1996) and Westhead and Cowling (1998) underlined that the family business definition used by researchers can affect the sampling and research outcomes. In defining family business some researchers (Astrachan, Klein, & Smyrnios, 2002; Beckhard & Dyer, 1983; Churchill & Hatten, 1987) have used a uni-dimensional approach whereas others (Litz, 1995; Shanker & Astrachan, 1996; Westhead & Cowling, 1998) have used a multi-dimensional approach. The dimensions frequently used in defining family firms are presented in Table 2.5. These different approaches that exist in the domain of family businesses perhaps give rise to the lack of conceptual clarity. However, although it appears that researchers and academics have not reached consensus as to what exactly a family firm is, there are nevertheless commonalities among most of the definitions. Relating to consensus, the observation of Neubauer and Lank (1998) is relevant here, that little consensus on a definition is common in any young academic discipline like family business. Commonalities in the definitions include percentage of ownership, voting control, power over strategic direction, involvement of multiple generations and active management by family members (Shanker & Astrachan, 1996). Although it is acknowledged that defining a family business is the first and most obvious challenge facing family business research (Handler, 1989), researchers (Habbershon & Williams, 1999; Tagiuri & Davis, 1996) have commonly accepted that family firms are a unique form of business compared to non-family firms.
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TABLE 2-5 AUTHORS WHO HAVE CONTRIBUTED TO DEFINING FAMILY BUSINESS Dimensions
Authors
Uni-dimensional Ownership
Management
Family involvement
Existence of generational handover
Barnes & Hershon, 1976; Donckels & Frohlich, 1991; Davis & Harvestion, 1988; Lansberg et al., 1988; Littunen & Hyrsky, 2000 Barry, 1975; Davis & Tagiuri, 1983; Dreux, 1990; Filbeck & Lee, 2000; Ward, 1990 Astrachan et al., 2002; Beckhard & Dyer, 1983; Handler, 1990; Chua et al., 1999; Chrisman et al., 2003; Davis, 1983; Dyer, 2003; Steier, 2001; Upton et al., 2001 Churchill & Hatten, 1987; Donnelly, 1964; Sharma et al., 1997; Tan & Fock, 2001
Multi-dimensional
Ownership and management
Ownership, management and an extra dimension
Carsrud, 1994; Corbetta, 1995; Covin, 1994; Donckels & Lambrecht, 1999; Dyer, 1986; Fiegener et al., 1994; Gallo & Sveen, 1991; Ginebra, 1997; Holland & Oliver, 1992; Kelly et al., 2000; Klein, 2000; Leach et al., 1990; Lansberg & Astrachan, 1994; Lank et al., 1994; Lyman, 1991; Pratt & Davis, 1986; Stern, 1986; Rosenblatt et al., 1985; Stavrou & Swiercz, 1998; Shepherd & Zacharakis, 2000; Tsang, 2001, 2002 Westhead & Cowling, 1998; Welsch, 1993. Amat, 1998; Astrachan & Kolenka, 1994; Cabrera & Garcia, 1999; Cadieux et al., 2002; Hall et al, 2001; Handler, 1989; Lea, 1993; Litz, 1995, 1997; Shanker & Astrachan, 1996; Sirmon & Hitt, 2003; Ward, 1987; Westhead et al., 1996
Source: Martos (2007), p. 477.
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2.4.3 UNIQUE NATURE OF FAMILY FIRMS Family firms are a unique species in terms of their resources and capabilities. Many scholars (Chrisman, Sharma, & Taggar, 2007; Chua et al., 1999; Habbershon & Williams, 1999; Habbershon, Williams, & MacMallan, 2003; Shanker & Astrachan, 1996; Sirmon & Hitt, 2003; Tagiuri & Davis, 1996; Wortman, 1994) maintain that family firms’ uniqueness arises from family involvement in the business, which has been referred to as familiness. Habbershon and Williams (1999) described familiness as the unique bundle of resources which results from the interaction among the family, individual members and the business itself. They underlined that familiness would help to gain competitive advantage for family firms over non-family firms (see Figure 2.4). FIGURE 2-4
FAMILINESS AND COMPETITIVE ADVANTAGE
Source: Habbershon & Williams (1999), p. 13.
Family firms are largely influenced by the owning family, whose norms, values, attitudes, and aspirations contribute to shaping the direction of the firm (Sharma et al., 1997). In their bivalent attribute model, Tagiuri and Davis (1996) described how family firms share common characteristics and culture as a result of interacting and overlapping 2-38
domains of family, ownership and management. It is acknowledged that these interactions facilitate the transfer of resources across the systems and across generations. Unlike non-family firms, family members in family firms are involved in the business affairs and can therefore influence the business in a number of ways. The literature suggests that family members exert an influence on a business through three overlapping but distinctive systems, the business, ownership and family (Tagiuri & Davis, 1996, see Figure 2.5). Tagiuri and Davis claimed that the degree to which family and ownership overlap the business systems indicates the degree of influence that a family has over the firm. FIGURE 2-5
FAMILY BUSINESS SYSTEMS
. Source: Tagiuri & Davis (1996), p. 202.
Recognising the owning family’s dominant role in the business, Astrachan, Klein and Smyrnios (2002) proposed three dimensions, power, experience and culture, in what they labelled the F-PEC scale, which captures the family influence in family firms. Power refers to dominance exercised through financing the business (e.g., shares held
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by the family) and through leading and/or controlling the business through management and/or governance participation by the family. The maximum family influence would be 100% where the family holds all shares, and all management personnel as well as all governance board members are family members. The proportion of shareholdings and the number of family members involved on the management/governance boards affect the degree of influence over the business. Experience refers to the summation of experience that the family brings to the business. It is operationalised via the number of generations in charge in ownership and management. Culture refers to the values and commitment that the family brings (Astrachan et al., 2002; Klein, Astrachan, & Smyrnios, 2005) to the business. It is argued that if many generations are involved in the business, their influence over the business is higher by virtue of the experience accumulated over the generations. Highlighting the unique nature of family businesses, some researchers (Habbershon & Williams, 1999; Habbershon et al., 2003) have argued that family firms have a capacity to generate a competitive advantage over non-family firms because of family members’ commitment towards a long-term orientation. Furthermore, scholars have contended that a family firm’s long-term orientation is backed by family members’ shared vision and strong sense of mission (Arregle, Hitt, Sirmon, & Very, 2007; Chua et al., 1999; Le Breton-Miller & Miller, 2006; Ward, 2002), relationship-oriented culture (Miller & Le Breton-Miller, 2005; Stavrou, Kleanthous, & Anastasiou, 2005; Upton, Teal, & Felan, 2001) and the necessity to continue the business as a family economic unit (Miller et al., 2008). On the other hand, others have argued that the involvement of family members in the firm can give rise to competitive disadvantage as a result of inward-looking strategies 2-40
(Colli, 2003; Gallo & Pont, 1996; Robert, 1964), nepotism (Chrisman et al., 2005; Colli, 2003; Robert, 1964), altruism (Chrisman et al., 2005; Schulze, Lubatkin, & Dino, 2003) and entrenchment (Gallo & Vilaseca, 1998; Gomez-Mejia, Nunez-Nickel, & Gutierrez, 2001; Morck & Yeung 2004b). However, collectively researchers are in agreement that the family involvement in family firms has a significant bearing on the firms’ strategic direction and continuity. The next section provides an overview of Australian family businesses, on which this research is based.
2.4.4 FAMILY BUSINESS IN AUSTRALIA 2.4.4.1
INTRODUCTION
Family businesses are significant contributors to the wealth of the Australian economy. Recent national survey findings show that family businesses account for 40% of private sector output and generate more than half of Australia’s employment (Smyrnios & Dana, 2006). In addition, it has been estimated that the total wealth of family businesses in 2006 was about A$4.3 trillion, which represents a greater value than the total of the Australian Security Exchange (ASX) market capitalisation of all listed companies plus the total value of all managed funds in Australia (PwC, 2007). Research further indicates that over half of Australia’s top 500 private companies are family owned (Smyrnios & Dana, 2006).
A great majority of family businesses are SMEs: however, families can be found in large public companies. Over 97% of all businesses in Australia are SMEs and they employ more than 3.5 million people (Clarke, 2006). It is estimated that about two-thirds of these businesses are family-controlled. Family ownership is common in industries such 2-41
as printing, publishing, construction and footwear (Kotey, 2005). Moreover, based on conservative estimates from the Australian Bureau of Statistics’ 1997/98 in which a narrow definition is adopted, Moores and Mula (2000) suggest that at least half of all Australian businesses are family-owned. In fact, an examination of companies listed on the ASX based on the criteria of dominant share ownership and presence of founding family member on the board or management as CEO or chairperson has shown that about 15% of publicly-held companies are family-controlled according to the Australian Centre for Family Business (ACFB). Some of these firms are among the world’s largest business organisations, examples being Publishing and Broadcasting Ltd. and News Corporation. Figure 2.6 shows the dominance of family businesses in the Australian business arena. FIGURE 2-6
DOMINANCE OF FAMILY BUSINESSES IN AUSTRALIA
Rural business (# 0.10 Mn) Non-family (85%) Businesses in Australia (# 1.96 Mn)
Listed (# 2130)
Family (15%) Non-rural business (# 1.82 Mn)
67% of all businesses Family (67%) Non-listed (# 1.81 Mn)
Non-family (33%)
Sources: Australian Bureau of Statistics (2007), Australian Security Exchange (2007), Australian Centre for Family Businesses (2007)
Recognising the significance of family businesses to the Australian economic and business landscape, several initiatives have been taken at institutional level to develop 2-42
and promote family business: the establishment of the ACFB
4
and Family Business
Australia (FBA)5 are central in this regard. 2.4.4.2 FAMILY SMES In studying family SMEs, it is important to understand what SMEs are, because family SMEs are a sub-set of SMEs. However, there is no uniform definition for SMEs either nationally or internationally. They constitute a diverse and dynamic group of enterprises. Given their diversity, most countries use broad definitions to capture the basic characteristics of SMEs. Criteria used include the number of employees, invested capital, turnover and industry type. However, the main criterion that most countries use for statistical purposes is the number of persons employed. Table 2.6 shows examples of international SME definitions in number of employees. TABLE 2-6 SMES BY NUMBER OF EMPLOYEES Small
Medium
Large
European Commission
< 50
< 250
–
United Kingdom
< 50
< 250
250+
Australia
5–19
20–200
200+
Source: Ministry of Economic Development (MOED) New Zealand (2006), p. 35 In this research the ABS definition of SMEs is used to identify Australian SMEs. The ABS (2000a, 2000b) defines SMEs as non-agricultural firms employing more than 4 and
4
The ACFB (Australian Centre for Family Business) was established in 1994 within the Faculty of Business at Bond University, with the objectives of encouraging family business research and education, and establishing networking opportunities for Australian family firms as a forum for exchanging ideas for improving their businesses.
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fewer than 200 people. Thus, for the purpose of this research, a family SME is defined as a firm employing more than 4 and fewer than 200 employees and the following three criteria hold true: (a) owners/managers regard their enterprises as a family firm, (b) 50% or more of the ownership is held by a single family and, (c) at least one director/manager in the firm is from the family. Several researchers (Carsrud, 1994; Cooper, Upton, & Seaman, 2005; Graves & Thomas, 2004; Kotey, 2005; Ram & Holliday, 1993) have argued that the owner-manager’s perception of the firm as a family business is an important defining variable even though it contains an element of subjectivity. To eliminate this definitional subjectivity, family members’ involvement in management and share ownership are also integrated into the definition in this study. The family business definition used in this study would appear to be located within the “middle definition” of Shanker and Astrachan (1996). A major challenge for family SMEs throughout the world is maintaining their growth and survival in the competitive environment (Birdthistle & Fleming, 2005; Ward, 1988; Zahra et al., 2008), and this is common to the Australian context as well. Researchers (Chirico, 2008; Dodgson, 1993; Edmondson & Moingeon, 1998; Sinkula et al., 1997) have suggested that organisational learning is one of the strategies that a firm can adopt to cope with these challenges. It is argued that knowledge that comes from organisational learning promotes organisational innovation (Baker & Sinkula, 1999; Calantone et al., 2002; Hurley & Hult, 1998; Salavou et al., 2004; Therin, 2002) whereby firms have the ability to maintain their competitive position. However,
5
FBA (Family Business in Australia), a national, member-based, not-for-profit organisation, was established in 1998 with the purpose of improving the effectiveness of Australian family businesses.
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organisational learning research has failed to properly consider SMEs, despite a number of researchers suggesting that the capability to learn organisationally is instrumental to the success of SMEs (Anderson & Skinner, 1999; Gibb, 1997). Thus, this study is designed to investigate the relationships between organisational learning, innovation and firm performance in family SMEs. In addition, the same relationships are examined to determine whether family SMEs display any innovation and performance differences from non-family SMEs in light of organisational learning and innovation. From a strategic perspective, the comparison is important to understanding whether the unique attributes of family businesses have a positive impact on the outcomes of firm strategies. In sum, it can be concluded that organisational learning and innovation are broad and multifaceted concepts and are affected by many factors. Organisational learning studies have shown that creating an organisational environment that promotes learning enhances a firm’s innovation and helps a firm to maintain its competitive position. Acknowledging this phenomenon, in the next section, the emergent conceptual framework underlying this research and the research hypotheses are presented to explore the relationships between organisational learning, innovation and firm performance in family SMEs and the intervening effect of innovation between organisational learning and firm performance. Further, hypotheses are developed to examine the effects of organisational learning, innovation and performance between family and non-family SMEs.
2.5 CONCEPTUAL FRAMEWORK AND HYPOTHESES 2.5.1 INTRODUCTION The relevance of organisational learning to improving organisational innovation and performance has received considerable attention. Despite the growing interest in
2-45
organisational learning on innovation and firm performance, no empirical research has emerged that explores the links between these notions in the context of family SMEs. In this research a conceptual framework is developed to explore the relationships between (a) organisational learning and innovation, (b) organisational learning and firm performance, (c) innovation and firm performance and, (d) the indirect (intervening) effect of innovation between organisational learning and firm performance in family SMEs. In addition, the effects of organisational learning, innovation and firm performance between family and non-family SMEs are also explored. Figure 2.7 presents the conceptual framework of the study. FIGURE 2-7
CONCEPTUAL FRAMEWORK – ORGANISATIONAL LEARNING, INNOVATION AND FIRM PERFORMANCE
2.5.2 ORGANISATIONAL LEARNING AND INNOVATION Knowledge and skills are critical to the process of innovation. Numerous researchers (Baker & Sinkula, 1999; Huber, 1998; Kieser & Koch, 2008; Nonaka, 1991) have suggested that a relationship exists between organisational learning and innovation. Stata (1989) maintained that organisational learning is the principal process by which organisational innovation occurs. Similarly, Hurley and Hutt (1998) argued that if 2-46
learning is to appear in new behaviour, then organisational learning is synonymous with the capacity to innovate. Further, they found a strong connection between the development of people and the innovativeness of the culture, specifically that the more an organisation encourages members to learn and develop and influence group decisions, the more innovative that organisation is. Moreover, Baker and Sinkula (1999) argued that organisational learning reflects the degree to which firms are committed to systematically changing fundamental beliefs and practices. However, despite the fact that learning (knowledge and skills) is more and more viewed as a central driver of innovation, it is not yet fully understood how these factors affect innovation. For instance, Moorman and Miner (1997) studied the link between existing knowledge and new product innovation and found an insignificant relationship between the two variables.
Although the concept of organisational learning is broad and multifaceted, several researchers have developed a number of instruments to measure it. For example, Goh and Richards (1997) identified five dimensions of organisational learning: clarity of purpose and mission, leadership commitment and empowerment, experimentation, transfer of knowledge, and teamwork and group problem solving. The notion of learning orientation6, which was developed by Sinkula, Baker and Noordewier (1997) has been used extensively in measuring organisational learning. It includes three dimensions: commitment to learning, open-mindedness and shared vision. Alternatively, Calantone
6
Habbershon (n.d.) presents a model on how family influence is embedded in six antecedent orientations that have been related to a firm’s innovation and how innovation is linked to performance outcomes in a firm. Notably, for this thesis “learning orientation” is postulated as a means by which “familiness” of a firm can impact performance via innovation.
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et al. (2002) viewed organisational learning as having aspects of commitment to learning, open-mindedness, shared vision and extra-organisational knowledge sharing. In this research three dimensions are used to explore organisational learning in SMEs. The notions of commitment to learning and shared vision are derived from the work of Sinkula et al. and extra-organisational knowledge sharing, which is here labelled networking, is derived from Calantone et al. However, the measuring variables for these three dimensions in this research are slightly different from their original variables, although conceptually they are closely related7. 2.5.2.1 COMMITMENT TO LEARNING AND INNOVATION The first dimension of organisational learning is commitment to learning. Commitment to learning concerns the values placed on learning activities within an organisation, and the extent to which these values are viewed as axiomatic for the firm (Senge, 1990). Employee training, management development, (Mavondo et al., 2005; Nevis et al., 1995; Senge, 1990;1996; Snell & Dean, 1992) and comparison of performance (Shrivastava, 1983, Habbershon & Williams, 1999), also termed “learning culture” (Goh & Richards, 1997; Senge, 1990), are some ways by which firms display their commitment to learning. Particularly employee training and management development have been shown to be associated not only with learning but also with the process of unlearning (Prahalad & Hamel, 1990). Unlearning relates to questioning existing assumptions and beliefs in the organisation; it promotes employees to think rationally and critically. The notion of unlearning is an essential element in organisational
7
The measurement variables used in this research are employee training, management development, comparison of performance, formal planning and networks.
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learning, to develop new ways of thinking and to question the status quo in the organisation. Lopez, Peon and Ordas (2005) underline that human resource development facilitates the creation of new knowledge and insights that encourage employees to question the long-held routines of the firm, with the potential for creating innovation in the firm. Organisations learn from environmental scanning (Ahituv, Zif, & Machlin, 1998; Albright, 2004; Habbershon & Williams, 1999; Shrivastava, 1983; Wang, 2008). Environmental scanning serves as an impetus for information acquisition and dissemination, an important starting point for learning (Wang, 2008). It broadens a firm’s knowledge on internal and external environments, a vital element for succeeding in a competitive market place. In this sense, comparison of performance in an organisation creates new knowledge that eventually has the potential to enhance the firm’s capacity for innovation. In this study, it is proposed that family SMEs which encourage employee training, management development and comparison of performance contribute to enhancing employees’ knowledge and skills, whereby innovation is likely to occur. Thus we propose:
Hypothesis 1 Commitment to learning (H1a - employee training, H1b - management development, and H1c - comparison of performance) is positively associated with innovation in family SMEs.
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2.5.2.2 SHARED VISION AND INNOVATION The second dimension of organisational learning is shared vision. A shared vision is a destination towards which everybody in the organisation strives (Garvin, 1993; Senge, 1990). It is a common understanding of where people want to go, what they and the organisation want to become. A shared vision aligns people to work towards the same goal, increasing their motivation as they see that everybody else is also working towards that destination. The development of a shared vision is an important step because it fosters a long-term orientation and demonstrates the importance of learning in relation to achievement of the firm’s vision (Senge, 1990). It provides an insight into the direction of organisational learning that helps in the understanding of what needs to be learned (Baker & Sinkula, 1999; Calantone et al., 2002; Senge, 1990; Sinkula, 1994). The formal planning process in an organisation is a mechanism for sharing the firm’s vision, and planning contributes to directing people towards a common goal. Stata (1989) argued that the benefits that accrue from formal planning are not just the strategies and objectives that emerge, but more importantly the learning that occurs during the planning process. Further, by fostering communication and interaction among all hierarchical levels, the formal planning process helps organisations to acquire and share knowledge (Sadler-Smith et al., 2001), and the knowledge acquired has the potential to enhance organisational innovation. Thus we propose:
Hypothesis 2 Shared vision (formal planning) is positively associated with innovation in family SMEs.
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2.5.2.3 NETWORKING AND INNOVATION The third dimension of organisational learning is networking. The impact of sharing knowledge among individuals is notable in the organisational learning literature (Dodgson, 1993; Nevis et al., 1995; Senge, 1990; Stata, 1989). Researchers (Huber, 1991; Levitt & March, 1988) have acknowledged that networking sometimes called external relations, of firms among industry, trade associations and other forms of association create learning by facilitating the sharing of knowledge, providing a means for organisations to learn from the experience of others in the industry. Pittaway, Robertson, Munir, Denyer and Neely (2004) highlight that networks are critical for accessing knowledge to create in-house innovations and also important for learning about innovative work practices that other organisations have developed or adopted. Networks promote social interaction generating trust and reciprocity that are conducive to knowledge transfer. Moreover, studies highlight that firms that do not network possess much lower levels of competence in innovation than firms that do network (Ritter & Gemünden, 2003). However, Harris, Coles and Dickson (2000) found that although inter-firm networking can facilitate the innovation process, it will not necessarily lead to innovation success.
As far as SMEs are concerned, networking activities are of particular importance to them to offset potential fragility engendered by the liability of small organisational size, acting as the key determinant of organisational development. For instance, Nahapiet and Ghoshal (1998) described how networks create favourable conditions for a firm’s exchange of knowledge and creation of new knowledge. Further, it has been argued that individual and network contacts may be an important source of new ideas, and networks have also been linked with the number of new opportunities perceived by entrepreneurs 2-51
(Arenius, 2005). The rationale is that networks can provide access to knowledge that is not currently possessed, bringing the potential for recognition of opportunities. However, few analyses address the connection between networks and innovation (Chiffoleau, 2005). Thus we propose: Hypothesis 3
Networking (external networks) is positively associated with innovation in family SMEs.
2.5.3 ORGANISATIONAL LEARNING AND FIRM PERFORMANCE Firm performance is arguably the most important construct in management research. A wide variety of definitions of firm performance have been proposed in the literature (Barney, 2007), with frequent reference to how efficiently and effectively a firm utilises its resources in generating economic outcomes. In the business strategy literature there are two major streams of thought on the determinants of firm performance (Hansen & Wernerfelt, 1989). One is based on factors that exist in the firm’s external environment, and the other is based on internal organisational factors. However, most research has highlighted the necessity of concentrating on strengthening internal organisational factors to improve organisational performance, rather than concentrating on external factors, which are often beyond the organisation’s control. Performance can be determined in various ways. It might stand for financial performance, market performance, customer performance or overall performance, at least. In this research performance is measured by financially-based performance measures. Organisational learning impacts on a firm’s performance (Baker & Sinkula, 1999; Calantone et al., 2002; Farrell, 1999; Sadler-Smith et al., 2001). Researchers (Nonaka, 1994; Senge, 1990) have addressed the relationship between a firm’s organisational learning and its performance, highlighting that learning creates new knowledge which 2-52
can help firms respond quickly to customers’ needs and industry changes. Baker and Sinkula (1999) and Farrell (2000) found that organisational learning yields promising results in organisations. By empirically testing a model of the antecedents and consequences of organisational learning, Farrell (1999) found that organisational learning has a positive effect on organisational commitment and esprit de corps, and on organisational performance. Using the model developed by Sinkula et al. (1997), Calantone et al. (2002) examined the relationships between organisational learning, firm innovation capability and firm performance in US technology companies. They found a positive relationship between organisational learning and firm performance. In sum, it can be stated that in an environment in which organisational learning is encouraged, individuals will be motivated, encouraged to learn, develop and share new skills and knowledge (Farrell, 1999; Nonaka, 1991), thereby facilitating an improved firm performance. Thus we propose: Hypothesis 4 Commitment to learning (H4a - employee training, H4b - management development, and H4c - comparison of performance) is positively associated with performance in family SMEs. Hypothesis 5 Shared vision (formal planning) is positively associated with performance in family SMEs. Hypothesis 6 Networking
(external
networks)
is
positively
associated
with
performance in family SMEs.
2.5.4 INNOVATION AND FIRM PERFORMANCE Researchers have hypothesised innovation as one possible mechanism by which firms can gain a competitive advantage in the marketplace through unique organisational 2-53
resources (Barney, 1991; Damanpour & Evan, 1984). Adoption of an innovation is expected to result in organisational change that might affect the firm’s performance (Rothwell, 1992). Research supports the argument that effective innovation serves as a key instrument for firm performance (Baker & Sinkula, 1999; Calantone et al., 2002; Craig & Dibrell, 2006; Damanpour & Evan, 1984). Innovation provides organisations with new means of meeting customers’ needs, which can lead to growth in sales and consequently enhance firm performance. Thus we propose: Hypothesis 7
Innovation is positively associated with performance in family SMEs.
However, although past research has investigated the direct relationship between organisational learning and firm performance and also innovation and firm performance none has empirically tested the indirect (intervening) effect of innovation between organisational learning and firm performance. Teece et al. (1997) highlighted that innovation can also lead to the development of key capabilities that can improve a firm’s performance. Thus, it is suggested that organisational innovation might partially affect the relationship between learning and firm performance. Hence, we propose: Hypothesis 8
The relationship between organisational learning and performance in family SMEs is positively intervened by firm innovation.
2.5.5 ORGANISATIONAL LEARNING, INNOVATION AND FIRM PERFORMANCE: FAMILY AND NON-FAMILY SMES
A key concern in the family business literature is whether family firms differ from nonfamily firms. Some studies (Daily & Thompson, 1994; Ward, 1988) have not identified any significant difference, whereas others (Gudmundson, Tower, & Hartman, 2003) have found that family firms differ from non-family firms in a number of key areas such 2-54
as strategic orientation and innovation. Studies highlight that the long-term nature of ownership (Miller & Le Breton-Miller, 2005; 2006; Zahra, Hayton, & Salvato, 2004), the kinship ties (Ward, 2002; Zahra et al., 2004), the family involvement (Astrachan et al., 2002; Chrisman et al., 2005; Habbershon & Williams, 1999), flexible organisational structures (Birdthistle & Fleming, 2005; Colli, 2003; Menkhoff & Kay, 2000), clan-like cultures (Moores & Barrett, 2002; Moores & Mula, 2000) and trust and enduring relationships (Alpay, Bodur, Yilmaz, Cetinkaya, & Arikan, 2008; Bopaiah, 1998; Gomez-Mejia et al., 2001; Miller & Le Breton-Miller, 2005; Palmer & Barber, 2001) that are unique to family firms allow them to dedicate the resources required for innovation, thereby fostering entrepreneurship and firm performance. Further, some research suggests that family firms could be more innovative and aggressive in their markets due to their relatively smaller size, greater local market knowledge, and relative financial independence compared to very large national companies (McCann, LeonGuerrero, & Haley, 2001). However, other research has shown that over time some family firms become more conservative (Zahra et al., 2004) or inward-looking (Colli, 2003). Given the potential differences between family and non-family firms (Sharma et al., 1997), it is important to test empirically whether the impact of organisational learning on innovation and firm performance in family SMEs differs from that in non-family SMEs. We hypothesise that the relationships between organisational learning, innovation and firm performance in family SMEs are stronger than in non-family SMEs because of their distinctive characteristics which can shape strategic choices and processes (Sharma et al., 1997). This investigation will make a contribution to expanding our understanding of differences between family and non-family firms
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(Chrisman, Chua, & Sharma, 2003) in terms of strategic orientation and outcomes. To explore the potential differences the following hypotheses are tested in this research:
Hypothesis 9
The relationship between commitment to learning (H9a - employee training, H9b - management development, and H9c - comparison of performance) and innovation is stronger in family SMEs than in nonfamily SMEs.
Hypothesis 10 The relationship between shared vision (formal planning) and innovation is stronger in family SMEs than in non-family SMEs. Hypothesis 11 The relationship between networking (external networks) and innovation is stronger in family SMEs than in non-family SMEs. Hypothesis 12 The relationship between commitment to learning (H12a - employee training, H12b - management development, and H12c - comparison of performance) and performance is stronger in family SMEs than in nonfamily SMEs. Hypothesis 13 The relationship between shared vision (formal planning) and performance is stronger in family SMEs than in non-family SMEs. Hypothesis 14
The relationship between networking (external networks) and performance is stronger in family SMEs than in non-family SMEs.
Hypothesis 15
The relationship between innovation and performance is stronger in family SMEs than in non-family SMEs.
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The relationships of the hypotheses developed in this study are depicted in Figure 2.8.
FIGURE 2-8 CONCEPTUAL FRAMEWORK WITH HYPOTHESES
2.6 CHAPTER SUMMARY In this chapter, the literature pertaining to organisational learning, innovation and family business was reviewed. The importance of organisational learning for innovation and firm performance was highlighted. The study identifies five variables to measure organisational learning: employee training, management development, comparison of performance, formal planning and networks. The first three variables relate to commitment to learning and the fourth relates to shared vision. The fifth variable, involvement
with
external
networks,
is
used
to
determine learning from
relationships/experience of industry. Subsequently, a conceptual framework was developed and presented to illustrate the associations among organisational learning, innovation and firm performance. Finally, based on the conceptual framework and 2-57
previous literature, fifteen main hypotheses were developed, of which eight relate to organisational learning, innovation and firm performance in family SMEs. The final seven hypotheses compare the effects of organisational learning, innovation and firm performance between family and non-family SMEs. The links between the research questions and hypotheses are shown in Table 2.7. The next chapter outlines the research method. TABLE 2-7 RESEARCH QUESTIONS AND HYPOTHESES Research Question (RQ)
RQ – 1: Does organisational learning in family SMEs affect firm innovation?
Hypotheses H1 Commitment to learning (H1a - employee training, H1b - management development, and H1c - comparison of performance) is positively associated with innovation in family SMEs. H2 Shared vision (formal planning) is positively associated with innovation in family SMEs. H3
RQ – 2: Does organisational learning in family SMEs affect firm performance?
Networking (external networks) is positively associated with innovation in family SMEs.
H4 Commitment to learning (H4a - employee training, H4b - management development, and H4c - comparison of performance) is positively associated with performance in family SMEs. H5 Shared vision (formal planning) is positively associated with performance in family SMEs. H6 Networking (external networks) is positively associated with performance in family SMEs
RQ – 3: (a) Does innovation in family SMEs affect firm performance? and, (b) Is the relationship between organisational learning and firm performance intervened by
H7 Innovation is positively associated with performance in family SMEs. H8 The relationship between organisational learning and performance in family SMEs is positively intervened by firm innovation. 2-58
innovation?
H9 The relationship between commitment to learning (H9a - employee training, H9b management development, and H9c comparison of performance) and innovation is stronger in family SMEs than in non-family SMEs.
H10 The relationship between shared vision (formal planning) and innovation is stronger in family SMEs than in non-family SMEs H11 The relationship between networking (external networks) and innovation is stronger in family SMEs than in non-family SMEs.
RQ – 4: Do these relationships and patterns in family SMEs differ from those in non-family SMEs?
H12 The relationship between commitment to learning (H12a - employee training, H12b management development, and H12c comparison of performance) and performance is stronger in family SMEs than in nonfamily SMEs. H13 The relationship between shared vision (formal planning) and performance is stronger in family SMEs than in non-family SMEs. H14 The relationship between networking (external networks) and performance is stronger in family SMEs than in non-family SMEs. H15 The relationship between innovation and performance is stronger in family SMEs than in non-family SMEs.
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CHAPTER THREE 3. RESEARCH METHOD 3.1 INTRODUCTION The previous chapter described organisational learning and its likely impacts on innovation and firm performance, and then developed a conceptual framework and testable hypotheses. The present chapter outlines the research method used to empirically test the hypotheses addressing the research questions posed in the first chapter. This chapter consists of four sections including this introduction. Section 3.2 details the research design, which includes data collection, sample selection and operationalisation of the variables. Section 3.3 details the statistical techniques employed in the research. Section 3.4 presents the chapter summary.
3.2 THE RESEARCH DESIGN
3.2.1 DATA COLLECTION – BUSINESS LONGITUDINAL SURVEY The data employed in this research were drawn from the Business Longitudinal Survey (BLS) conducted by the Australian Bureau of Statistics (ABS) on behalf of the federal government over the four financial years 1994/95 to 1997/98. The ABS designed this survey with the objectives of providing information on the growth and performance of Australian employing businesses and identifying selected economic and structural characteristics of these businesses (ABS, 2000a, p.2). The scope of the BLS is all employing industries in Australia excluding agriculture, forestry and fishing (ANZSIC8
8
Australian and New Zealand Standard Industrial Classification.
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division A), electricity, gas and water supply (ANZSIC division D), communication services (ANZSIC division J), government administration and defence (ANZSIC division M), education (ANZSIC division N), health and community services (ANZSIC division O), other services (ANZSIC subdivision 96), private households employing staff (ANZSIC subdivision 97), and libraries, museums, and parks and gardens (ANZSIC groups 921, 922 and 923)(ABS, 2000a , p.3). The ABS Business Register was used as the population frame for the survey, with approximately 13,000 business units being selected for inclusion in the 1994/95 survey. For the 1995/96 survey, a sub-sample of 4,700 firms (Dockery, 2001) from the original selections for 1994/95 was selected and this was supplemented with a sample of new business units added to the ABS Business Register during 1995/96. The sample for the 1996/97 survey was again in two parts. The first formed the longitudinal or continuing part of the sample, consisting of all the remaining live business units from the 1995/96 survey, and the second part was a sample of new business units added to the ABS Business Register during 1996/97. A similar procedure was followed for the 1997/98 survey. The BLS sample by year and panel status is presented in Table 3.1. TABLE 3-1 BLS SAMPLE BY YEAR AND PANEL STATUS 1994/95
1995/96
1996/97
1997/98
8375
4543
4657
4658
New firms
0
484
409
464
Ceased operating
1
488
371
409
8376
5515
5437
5531
8.8%
6.8%
7.4%
Continuing firms
Total sample Business attrition rate
The BLS is not a completely random sample. The original population (for 1994/95) was stratified by industry and business size. Then, in the second phase of the survey, the 3-61
sample was further stratified by innovation status, exporting status and growth status of the business (ABS, 2000a, p.18). The statistical unit for the survey is referred to by the ABS as the management unit. The management unit is the highest level accounting unit within a business, having regard for industry homogeneity, for which detailed accounts are maintained (ABS, 2000a, p.3). In most cases this unit is the legal entity owning the business (for example, sole proprietorship, partnership, trust, company etc.). In the case of large diversified businesses, however, there may be more than one management unit, each coinciding with a division or line of business. Data collection in the BLS was achieved through self-administered, structured questionnaires predominantly containing closed questions. Copies of the questionnaires used in each of the four years are not included in the present study but are available from the ABS - http://www.abs.gov.au. The questionnaires were piloted prior to their first use, and were then progressively refined in the light of experience gained in each year of the survey. The survey included ongoing questions as well as one-off questions, in order to collect information relating to matters of policy interest to the federal government at the time of data collection. Various imputation techniques, including matching with other data files available to the ABS, were employed (McMahon, 2001b) to address the issue of missing data in the surveys. Although some data items collected varied from year to year, most of the items collected fall into the following broad categories: (i)
Background characteristics of the business, such as business locations and activity, including industry, years of operation, legal status, foreign
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ownership, family business, managerial experience and qualifications, union membership, employment and employment arrangements (ii)
Business links and networks, including formal and informal information networks
(iii)
Business operations, including number of days a business operates, types of business practices, employee training, major changes in business operations, business planning and business intentions
(iv)
Innovation, including a measure of whether any type of product/process innovation had been undertaken in the survey year and the amount of expenditure on such innovation
(v)
Participation in government programmes such as Export Finance and Insurance Corporation (EFIC) facilities, Austrade programmes, and government employment programmes
(vi)
Value and extent of exporting activities
(vii)
Use of information technology, including the type and extent of use and for what purposes
(viii) Financial information, including business income, expenditure, profit and loss, assets and liabilities, and equity finance. To ensure the confidentiality of unit records, the ABS adopted several mechanisms including restricted access to some data (for example, industrial classification, geographical indicators and enterprise age), and omission of some fields from the records (for example, owner’s equity, foreign ownership and methods of exporting, 3-63
business disputes). Moreover, all financial variables were subject to perturbation, a process in which values are varied slightly to provide further confidentiality protection. That process was applied to each financial variable separately for each year. In addition, firms employing 200 and more than 200 employees, which the ABS categorised as large businesses, were removed from the Confidential Unit Record File (CURF) (Hawke, 2000). The major strengths of this survey are its information richness, full coverage of the country, response rate over 90% (Hawke, 2000; McMahon, 2001a, 2001b) and longitudinal data. This is one of the few longitudinal surveys of SMEs in the world (Pink & Jamieson, 2000). Concerning its data, the inclusion of financial information of SMEs provides a major strength to this database as financial information of SMEs is hardly accessible to researchers. The significance and relevance of the database to researchers is shown by its considerable use in research (see Appendix B). The BLS is therefore ideal for analysing the important changes, strategies and the status quo of the Australian SME sector (Hawke, 2000).
3.2.2 SAMPLE SELECTION The BLS data used in this study were included in a Confidential Unit Record File (CURF) released by the ABS on CD-ROM in December, 1999. The CURF contains data on 9,732 business units employing fewer than 200 people, which broadly represents SMEs in the Australian context. The following criteria were used in selecting the sample for the present study: 1. Legal status – Only legally incorporated SMEs were selected for the study. The main reason for this selection is that incorporated firms are formally organised 3-64
enterprises and are more likely to be growth oriented (Freedman & Godwin, 1994; Hughes & Storey, 1994). 2. Manufacturing firms – The research was confined to the manufacturing SMEs of the BLS CURF for several reasons. First, manufacturing SMEs are the major segment of business in the Australian economy according to the BLS. Second, over the last few decades, the performance of Australian manufacturing sector has been a major preoccupation of policy-makers and the federal government (ACCI, 2007; McMahon, 2001b). Moreover, this sector is continually challenged by the volatile economy, growing global competition and changing market conditions (Prime Minister’s Science Engineering and Innovation Council, 2007). The Australian and New Zealand Standard Industry Classification (ANZSIC) (2 digit level) was used to identify the manufacturing SMEs in the study. It is reported that on average there were 2,149 manufacturing SMEs in the BLS CURF, representing approximately 35% of all businesses contained in the file. 3. Presence of all variables in the conceptual model in all four years – In selecting the years of surveys for the study, there was a need to verify whether all variables included in the model were included in all four years. It was identified that a question relating to networks with other businesses – one of the variables in this study – was asked in every BLS except that conducted in 1994/95. Due to absence of the network question in 1994/95, this study limited the analysis to data collected from the financial year surveys 1995/96 to 1997/98 only.
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4. Firm’s presence in all three years – Firms operating in all three years of the study were selected. To ensure that firms included in the study were operational over the three-year period, firms that reported no assets and/or employees and/or no sales in any year were excluded. 5. Family firms – The following criteria were used to identify family firms: (a) from the manufacturing SMEs, firms that answered in the affirmative to the Australia Business Survey (BLS) question: “Do you consider this business to be a family business?” and (b) firms in which 50% or more of the ownership was held by a single family; and at least one director in the firm was from that family. Manufacturing SMEs that did not satisfy these criteria were grouped as nonfamily manufacturing SMEs. Smith (2006) used a similar approach for selecting family controlled manufacturing SMEs in Australia.
Based on these criteria, 222 manufacturing SMEs consisting of 104 family firms and 118 non-family firms were selected for this study. The statistical attributes of the data contained in the BLS and the selected sample are illustrated in detail in Chapter Four.
3.2.3 OPERATIONALISATION OF THE VARIABLES Several researchers (Gautam & Riitta, 2001; Tsai, 2001) have contended that organisational learning has a lag effect on innovation and firm performance. Similarly, innovation research (Damanpour & Evan, 1984; Tsai, 2001) highlights the lag effect of innovation on firm performance. However, there is no consensus regarding the lag period between organisational learning, innovation and performance, and different studies have used different lag periods based on the data (Tsai, 2001). In this research, we lagged the effect of organisational learning on innovation by one year and the effect 3-66
of innovation on firm performance by a further one year, as the data covered only a three year period. In assessing firm performance in the light of innovation, firm performance in the year being analysed was based on the innovation responses in the previous year. Likewise, innovation in the year being analysed was based on the organisational learning responses in the preceding year. Thus, overall firm performance (combining the direct and indirect effects of organisational learning) in the year being analysed was based on the innovation responses in the preceding year and the organisational learning one year before the preceding year. Accordingly, in testing the research hypotheses, firm performance in 1997/98 was regressed with innovation in 1996/97 and with organisational learning in 1995/96. Mathematically, the relationships discussed above can be expressed as:
FPt
= α + β Inn t −1 ……………………………………… (1)
Innt −1 = υ + γ OL t −2 ………………………………………. (2)
FPt
= α + β Inn t −1+ γ OLt − 2 ……………………………. (3)
where: PFt
=
Firm performance 1997/98
Innt −1
=
Innovation 1996/97
OLt − 2
=
Organisational learning 1995/1996
α and γ
=
Intercepts of respective equations
In testing the hypotheses, this study used (a) performance data from the 1997/98 survey, (b) innovation data from the 1996/97 survey and (c) organisational learning data from
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the 1995/96 survey. Appendix C presents the BLS questionnaire items used in this study. In this section we describe how the independent, intervening, and dependent variables that were illustrated in the conceptual framework are operationalised. In addition, firm size, age and past performance that are likely to control the relationships between constructs in the model are discussed.
3.2.3.1 ORGANISATIONAL LEARNING Organisational learning is the independent construct in this research. As discussed, Chapter Two identified three organisational learning dimensions: commitment to learning, shared vision and networking. Commitment to learning is operationalised through three variables: employee training, management development and comparison of performance of the firm with competitors. Employee training is captured in this study in the form of employee training provided by the firms to their employees. The BLS Likert-type question relating to employee training which included on-the-job training is used to determine the intensity of training. Management development is captured through the Likert-type question in the BLS relating to management training in the firm. In the present study, for the purpose of testing the hypotheses, the responses of these two items are recoded as -1 for a decrease in training, 0 for no change and +1 for an increase in training. Dockery (2001) used a similar approach for recoding the changes in training in his study on training, innovation and business performance titled “An analysis of the Business Longitudinal Survey”. The third variable, comparison of performance, is captured by the dichotomous type question in the BLS relating to comparison of firm
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performance with competitors. Employee training, management development, and comparison of performance are included in hypotheses H1, H4, H9 and H12. Shared vision is captured by the presence of formal planning in the firms. To ascertain whether a firm engaged in formal planning, the survey question that asked whether the firm had a formal business plan is used. Firms that had engaged in a formal business plan are coded ‘1’ and firms that did not are coded ‘0’. The formal planning variable is included in hypotheses H2, H5, H10 and H13. Networking is captured through the firm’s engagement in networking activities. To ascertain whether a firm had been involved in networking activities, the survey question that asked whether the firm had engaged in any formal networks with other firms is used. In this study, networking is a binary variable wherein ‘1’ indicates that the SMEs engaged in networking and ‘0’ otherwise. The networks variable is included in hypotheses H3, H6, H11 and H14. 3.2.3.2 INNOVATION Innovation is the intervening variable in this research. An intervening variable is one that intervenes in the relationship between the independent and dependent variables, which helps in explaining the influence of the independent variable on the dependent variable (Sekaran, 2003). As discussed in Chapter Two, innovation in manufacturing SMEs is measured by their product and process innovation intensity. The BLS questions pertaining to innovation (research and development, acquisition of technology (patents, trademarks and licences), expenditure for tooling-up, industrial engineering and start-up, and expenditure on marketing of new or improved products) are used to determine the intensity of product
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and process innovation of firms in the sample. Marketing and technology expenditures are also included in the innovation expenditure as they are generally considered to be part of innovation (Olsen et al., 2006; Rogers, 1998). The product and process innovation, as a percentage of output, is computed as follows: P&P INN
(Inn − Cost)
= X100 Output
where: R&P INN
=
Product and process innovation intensity
Inn-Cost
=
Innovation expenditure
Output
=
Output of the firm in the given year.
Dividing the summation of innovation expenditure by output reduces the firm size effect and presents the relative value of process and product innovation intensity. Output in the equation is equivalent to sales plus ending inventory less beginning inventory. The process and product innovation variable is included in hypotheses H1-H3, H7-H11 and H15. 3.2.3.3 FIRM PERFORMANCE As explained in the discussion in Chapter Two, firm performance is the dependent/outcome variable which is the primary interest of the study. Broadly, firm performance can be measured in two forms: non-financial and financial. Non-financial measures are based chiefly on subjective information provided relevant to the firm’s state of affairs, whereas financial measures largely use the firm’s accounting information. In this research, a financially-based perspective is used for measuring firm performance, acknowledging the fact that the learning and innovation outcome ultimately leads to attainment of improved financial performance. To operationalise
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financially-based performance, return on total assets (ROTA) and growth of sales are employed. In this study ROTA is ascertained as: EBITDA ROTA = TA
ROTA
=
Return on total assets
EBITDA
=
Earnings before interest, tax, depreciation, and amortisation
TA
=
Total assets
where:
The growth of sales is computed as a percentage of changes in sales from year t-1 to t. Firm performance construct is included in hypotheses H4-H8 and H12-H15.
3.2.3.4 CONTROL VARIABLES Firm size, age and past performance are used as control variables to control firm effects on organisational learning, innovation and performance. However, although industry sector likely affects organisational learning, innovation and firm performance, due to unavailability of data in the BLS relating to industry sector it was not possible to control for this aspect in the present study. As a tool of protecting the confidentiality of the firms included in the database, the ABS does not disclose the specific sector within which each manufacturing firm operates. We recognise this to be a limitation of the present study. Firm size: Firm size is usually considered to be of importance in the context of the strategic decision-making involved in organisational learning. Child (1972) and Mintzberg (1973) have suggested that firm size affects managerial decisions. The effects of firm size on innovation have been investigated, but the results are mixed. Whereas some researchers (Cohen & Klepper, 1996b; Kimberly & Evanisko, 1981) have reported 3-71
a positive effect of firm size on innovation, others (Holmstrom, 1989; Martinez-Ros & Labeaga, 2002) have found a negative effect or no effect at all. In this study, firm size is included as a control variable and is operationalised using the natural logarithm of the firm’s total number of employees (Fombrun & Ginsberg, 1990; Karaevli, 2007; Tsai, 2001). A logarithm is used because number of employees is highly skewed among the firms in the study. Firm age: The study also controls for firm age, considering the fact that older firms might have well-established systems and procedures that promote greater organisational learning than in younger firms. Moreover, the learning curve experience prevailing in older firms provides more opportunity for learning, thereby contributing to improvement in the firm’s performance. As far as innovation is concerned, some studies (Calantone et al., 2002; Hansen, 1992; Heunks, 1998; Thornhill, 2006) have highlighted the existence of a relationship between firm age and innovation. Firm age in this study is measured by the number of years the firm had been in existence (Karaevli, 2007). However, the BLS measured firm age using an ordinal variable, reflecting age in three- and five-year intervals from 2 to 20 years, with two single categories for firms greater than 20 years of age and for firms less than 2 years. For firm age therefore, this study utilises five discrete categories and codes them as follows: Firm Age Category
Code
< 2 yrs
1
≥ 2 yrs and < 5 yrs
2
≥ 5 yrs and < 10 yrs
3
≥ 10 yrs and < 20 yrs
4
≥ 20 yrs
5
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Past performance: Several previous studies (Brush, Philip, & Hendrickx, 2000; Zahra et al., 2004) recognise that firm performance is likely to be influenced by prior performance. Similarly, some studies posit that when a firm performs well, financial slack increases and thus greater opportunities are created for innovation (Herold, Jayaraman, & Narayanaswamy, 2006) and learning (Kotaro, 1998). In this research past performance is included as a control variable to neutralise its effect on organisational learning, innovation and firm performance. Past performance is ascertained averaging the ROTA during the financial years 1995/6 and 1996/7. In summary, the overall model of the research in mathematical form can be presented as follows:
FPt = ά + β1 pro_pro_innt-1 + β2 emp_traniningt-2 + β3 mgt_devet-2 + β4 com_pt-2 + β5 formal planningt-2 + β6 networks,t-2 + β7 sizet + β8 aget + β9 p_per + έt where: FPt
Firm performance1997/98
ά
Intercept
β1 pro_pro_innt-1
Product and process innovation1996/97
β2 emp_trainingt-2
Employee training1995/96
β3 mgt_devet-2
Management development1995/96
β4com_pt -2
Comparison of performance1995/1996
β5 formal planning t-2
Formal planning1995/96
β6 networks t-2
Networking1995/96
β7 sizet
Firm size1997/98
β8 aget
Firm age 1997/98
β9 p_per
Past performance
έt
Error correction term
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3.3 STATISTICAL TECHNIQUES Statistical techniques are the tools by which researchers analyse data, test research hypotheses, and subsequently refine theories. The hypotheses and the characteristics of the data determine the types of analysis that need to be conducted. With this in mind, two main statistical techniques are used in the research. First, multiple linear regression analysis is used for testing the hypotheses relating to within family SMEs. Second, the Chow test is employed to measure any statistically significant differences in innovation and firm performance between family and non-family SMEs in the light of organisational learning and innovation. In addition, descriptive statistics are used to analyse and interpret the statistical attributes of the population, sample and variables.
3.3.1 MULTIPLE LINEAR REGRESSION ANALYSIS In this research, multiple linear regression analysis is the principal statistical technique used to test the hypotheses. Multiple linear regression analysis is a general statistical technique used to analyse the relationship between a single dependent variable and several independent variables (Hair, Black, Babin, Anderson, & Tatham, 2006). It is one of the most extensively used multivariate statistical techniques for testing hypotheses and predicting values for dependent variables. However, the purpose of using multiple linear regression analysis here is not to generate a model useful for predicting the performance of family SMEs, but to determine using hypothesis testing whether organisational learning affects firm innovation and firm performance and subsequently whether innovation affects firm performance. Accordingly, the research is designed to allow discussion of the quantitative results of the analysis in light of the significance of
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beta coefficients entered into the model, rather than to describe the accuracy and the fitness of the model. The generic form of a multiple linear regression is: Yi = β0 + β1Xi1 + β2Xi2 + β3Xi3 +………….. + βjXij + έi
where y is the dependent variable, Xi1 , …………, Xij are the independent variables, β0 is the constant , β1 ………, βj are the regression coefficients, notation i refers to the ith case in the n sample of observations, and έ represents an error term. The underlying assumptions of the linear regression, the linearity, normality and homoscedasticity are tested in continuous data used for the regression analysis. Linearity is the relationship between dependent and independent variables, representing the degree to which change in the dependent variable is constant across the range of values for the independent variable. Linearity is assessed by analysing the scatterplots of the variables. If nonlinearity is detected a data transform technique is used to convert the data into linear format. The most fundamental assumption in linear regression analysis is normality, which refers to the degree to which the distribution of data corresponds to a normal distribution (Hair et al., 2006). Normality can be checked using a box plot diagram and kurtosis and skewness testing. In this study the kurtosis and skewness are used to detect the normality of the variables. If non-normality is found, a data transformation technique is used to transform the data into normality. Consistent variance of the error term is associated with homoscedasticity. Homoscedasticity assumes that the dependent variable exhibits equal levels of variance across the range of predictor variables (Hair et al., 2006). Variability affects the standard
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error and makes hypothesis testing either too stringent or too insensitive. The Levene test is used to assess whether the variances are equal across any number of groups. Multicollinearity is another factor that needs to be taken into account in interpreting results, as it distorts the results of the regression. A multicollinearity problem arises when two or more independent variables are linearly related. This situation can be detected by analysing variation inflation factors (VIFs). A VIF value of 1.0 indicates that a variable is orthogonal to all other independent variables, implying that no multicollinearity exists. However, a common rule of thumb to indicate the existence of multicollinearity is a VIF value of 10 or higher (Lomax, 1992). The framework used in the research shows an indirect relationship between organisational learning and firm performance via innovation. Innovation is the intervening variable in the indirect relationship. Conceptually, intervening variables come between independent and dependent variables and represent the generative mechanism through which the independent variables influence the dependent variable. Baron and Kenny (1986) have discussed how the intervening effect is captured in multiple regression. In this study, a procedure suggested by Baron and Kenny (1986) and Frazier et al. (2004) is used to capture the intervening effect of innovation between organisational learning and firm performance. For this a series of three regression analyses is needed. The first is the regression of the intervening variable (innovation) on the independent variable (organisational learning). The second is the regression of the dependent variable (firm performance) on the independent variable (organisational learning), and the third is the regression of the dependent variable (firm performance) on both the independent (organisational learning) and the intervening (innovation) variables. 3-76
As Baron and Kenny (1986) suggested, intervening is established when several conditions are satisfied. First, the independent variable must significantly affect the intervening variable. Second, the independent variable must significantly affect the dependent variable. Third, the intervening variable must significantly affect the dependent variable but the effect of the independent variable on the dependent variable must be less in the third regression than in the second. Moreover, Baron and Kenny posited that the intervening effect is partial when the relation between the independent and dependent variables is significant in the third condition but at a reduced level compared with the second condition. The significance of the intervening effect in this study is measured using the Sobel test.
3.3.2 CHOW TEST A widely used test for comparing two regression models is the Chow test (Chow, 1960; Liao, 2004). The test determines whether the coefficients in a regression model are the same in separate sub-samples. In this research to determine the significance of the differences across family and non-family SMEs in the effect of organisational learning on innovation and firm performance and innovation on firm performance, the Chow test is used. The equation for the test is:
F=
(RSSR − RSSUR) / k F k n1 +n2 −2k (RSSUR) /(n1 + n 2 − 2k) ~
[(
)]
Where, RSSR
=
the sum of squared residuals from a linear regression in which b1 and b2 are assumed to be the same (restricted model).
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RSSUR
= the
sum of squared residuals from a linear regression of sample 1 (RSS1) and
sample 2 (RSS2) (Unrestricted model). n1
= Sample
size – Sample 1
n2
= Sample
size – Sample 2
k
=
the number of parameters estimated
The SPSS software (version 15) is used for regression analysis and for testing the underlying assumption of linear regression in this research.
3.4 CHAPTER SUMMARY This chapter outlined the research method used in the study. First, descriptions were presented of data collection, sample selection, and operationalisation of the variables. The features of the BLS were described, the most recent comprehensive longitudinal survey in Australia, data from which was used this research. Finally, the statistical techniques of the research were identified and discussed. The most appropriate techniques were identified as regression analysis and the Chow test. In the following chapter the research hypotheses are tested according to the statistical procedure discussed in the preceding sections, and results are presented and interpreted.
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CHAPTER FOUR 4. QUANTITATIVE ANALYSIS AND RESULTS 4.1 INTRODUCTION The purpose of this chapter is to present and analyse the empirical results of the study. The chapter consists of five sections including this introduction and then proceeds as follows. First, in Section 4.2 the demographic characteristics of the firms in the BLS are presented and described for the purpose of providing background information for the analysis. Section 4.3 presents some selective descriptive statistics of the sampled firms of the study to sketch a general picture of the data used. Section 4.4 reports the results of the hypothesis testing and examines the results. Finally, Section 4.5 presents the chapter summary.
4.2 DEMOGRAPHIC CHARACTERISTICS OF THE FIRMS IN THE BUSINESS LONGITUDINAL SURVEY9 4.2.1 INDUSTRY DISTRIBUTION OF FIRMS As discussed in Chapter Three, the BLS was conducted by the Australian Bureau of Statistics over the financial years 1994/95 to 1997/98 to identify selected economic, managerial and structural characteristics of Australian businesses. The BLS is the first official longitudinal survey of businesses in Australia and one of the few in the world (Pink & Jamieson, 2000). The corpus consists of 9,732 firms10 employing fewer than 200
9
Because the demographic characteristics of populations of the BLS are almost identical over years, the results presented in most Tables in Section 4.2 are limited to the 1997/98 survey data only. 10
9,732 firms comprised of 8,375 firms in 1994/95 survey; 484 new firms in 1995/96 survey; 409 new firms in 1996/97 survey and 464 new firms in 1997/98 survey.
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employees within the industries of mining, manufacturing, construction, wholesale trade, retail trade, accommodation, cafes and restaurants, transport and storage, finance and insurance, property and business services, cultural and recreational services, and personal and other services. Table 4.1 shows that the majority of the firms in the BLS fall into the manufacturing industrial category, which represents approximately 35% of the firms surveyed. The wholesale trade and the property and business services represent the second and third largest industrial categories respectively in the BLS, and mining is the smallest industry category, containing approximately 1% of the firms surveyed. The industry distribution of firms in the BLS based on the ANZSIC is presented in Table 4.1. TABLE 4-1 INDUSTRY DISTRIBUTION OF FIRMS 1994/95
1995/96
1996/97
1997/98
Industry No.
%
No.
%
No.
%
No.
%
60
0.7
53
1.1
61
1.2
67
1.3
Manufacturing
3076
36.7
1832
36.4
1804
35.6
1774
34.6
Construction
452
5.4
296
5.9
303
6.0
330
6.4
Wholesale trade
1074
12.8
744
14.8
770
15.2
768
15.0
Retail trade
899
10.7
525
10.4
546
10.8
558
10.9
315
3.8
207
4.1
200
3.9
209
4.1
Transport and storage
340
4.1
198
3.9
198
3.9
202
3.9
Finance and insurance
350
4.2
222
4.4
223
4.4
229
4.5
1397
16.7
718
14.3
722
14.3
737
14.4
185
2.2
118
2.3
122
2.4
127
2.5
Personal and other services
227
2.7
114
2.3
117
2.3
121
2.4
TOTAL
8375
100
5027
100
5066
100
5122
100
Mining
Accommodation, cafes and restaurants
Property and business services Cultural and recreational services
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4.2.2 FIRM SIZE AND AGE Tables 4.2 and 4.3 provide descriptive statistics pertaining to firm size and age, for all the industrial categories contained in the BLS in the financial year 1997/98. Although there are several alternatives for grouping businesses by size, Table 4.2 provides the number of firms in each industry category in terms of full-time employees. The ABS has also adopted total employment as the basis for classifying non-agricultural businesses by size, and the size categories used in this research are consistent with the ABS business size classifications. TABLE 4-2 DISTRIBUTION OF FIRMS BY NUMBER OF EMPLOYEES – 1997/98 FIRMS BY EMPLOYEES
Mean
Industry
1–4
5 – 19
20 < 200
Total
(Employees)
Mining
20
21
26
67
32
Manufacturing
338
578
858
1774
32
Construction
165
104
61
330
15
Wholesale trade
143
253
372
768
35
Retail trade
143
192
223
558
34
Accommodation, cafes and restaurants
53
91
65
209
34
Transport and storage
64
75
63
202
35
Finance and insurance
120
50
59
229
36
Property and business services
278
231
228
737
35
Cultural and recreational services
45
36
46
127
39
Personal and other services
49
45
27
121
31
1418
1676
2028
5122
36
TOTAL
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As Table 4.2 shows, 1,418 firms (27.7%) fell into the category of micro-sized firms on the basis of number of employees; that is, they employed fewer than five people. Firms employing from 5 to 19 people, categorised as small-sized firms, constituted 32.8% of the firms surveyed. Medium-sized firms, employing 20 to 199 people, constituted 39.6% of the firms surveyed. In total, the data show that 72.3% of the firms in the BLS in the financial year 1997/98 were SMEs. Table 4.2 further shows that the mean number of employees of all firms surveyed during the period was 36. However, the construction industry displayed a relatively low mean number of employees (15) compared to other industries. This may be because the construction industry uses “sub contractors”, and they are not classified as employees. Table 4.3 displays the age distribution of firms based on the duration of their existence since foundation. The data show that the majority of firms in the BLS had been in existence for over 10 years, indicating that the survey data are made up of reasonably established firms. For an example, in the 1997/98 survey 55% of firms had been established for over 10 years, 31% between 10 and 19 years and 24% for 20 years or more. Of the 5,122 firms surveyed in 1997/98, only 258 (5%) were less than 2 years old (see Table 4.3).
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TABLE 4-3 DISTRIBUTION OF FIRMS BY AGE – 1997/98 FIRMS BY AGE (IN YEARS)
Industry
Total 0.10) nor the moderating effects of equity capital on the relationship between organisational learning and innovation (employee training β = 0.006; p > 0.10, management development β = 0.028; p > 0.10, comparison of performance β = 0.003; p > 0.10, formal planning β = 0.003; p > 0.10 and networks β = -0.006; p > 0.10) were statistically significant. This does not support the moderating effects of resource availability; instead the results highlight the need to further explore the non-significant relationship between organisational learning and innovation. As indicated in Chapter Five, a highly likely reason for this lack of relationship could be the lack of KIUS in family SMEs.
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Similarly, the second additional test analysed the moderating effects of equity capital on the relationship between organisational learning and firm performance. The results in Table F.2 show a significant positive relationship between management development and firm performance (β = 0.172, p < 0.05) and further show that the relationship improved with the moderating effects of resource availability (β = 0.249, p < 0.01). Moreover, the results indicate that the model overall is significant (adjusted R2 = 0.199; F = 9.516; p 0.10, comparison of performance β = 0.035; p > 0.10, formal planning β = 0.150; p < 0.10 and networks β = -0.063; p > 0.10). Overall, these results reject the moderating effects of resource availability. This result also suggests that a highly likely reason for the lack of relationship between learning and firm performance could be the lack of KIUS in family SMEs.
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TABLE F-1 THE MODERATING EFFECTS OF EQUITY CAPITAL ON THE RELATIONSHIP BETWEEN ORGANISATIONAL LEARNING AND INNOVATION Control Firm size Firm age Past performance
Direct effects
-0.312*** (-3.322) -0.011
-0.296*** 0.011
0.011
(-0.116)
(0.120)
(0.120)
-0.081
-0.124*
-0.124*
(-0.861)
(-1.307)
(-1.307)
Employee training Management development Comparison of performance Formal planning Networks Equity capital
(-3.180)
(-3.180)
0.107
0.107
(1.071)
(1.071)
0.029
0.029
(0.308)
(0.308)
0.091
0.091
(0.963)
(0.963)
0.040
0.040
(0.410)
(0.410)
0.179**
0.179**
(1.921)
(1.921
-0.008
-0.008
(-0.073)
(-0.073)
0.006
Employee training x Equity capital
(0.062)
0.028
Management development x Equity capital
(0.292)
Comparison of performance x Equity capital
0.003 (0.025)
0.003
Formal planning x Equity capital
(0.026)
-0.006
Networking x Equity capital R2 R2 (Adjusted) F- value ∆R2 Max VIF
Moderating effects -0.296***
(-0.048)
0.098 0.089 11.036*** 1.000
0.129 0.112 7.508*** 0.032 1.008
0.129 0.112 7.508*** 0.032 1.008
N= 104, t values are in parentheses *** Significant at the 99% confidence interval (one-tailed) ** Significant at the 95% confidence interval (one-tailed) * Significant at the 90% confidence interval (one-tailed)
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TABLE F-2 THE MODERATING EFFECTS OF EQUITY CAPITAL ON THE RELATIONSHIP 16 BETWEEN ORGANISATIONAL LEARNING AND FIRM PERFORMANCE Control Firm size Firm age Past performance Innovation
Direct effects
Moderating effects
0.109
0.098
0.139*
(1.1270
(1.023)
(1.461)
-0.035
-0.045
0.004
(-0.3720
(-0.491)
(0.047)
0.088
0.123*
0.160**
(0.948)
(1.348)
(1.790)
0.360***
0.354***
(3.901)
(3.923)
Employee training Management development Comparison of performance Formal planning Networks Equity capital
0.024
0.344*** (3.895) 0.020
(0.233)
(0.215)
0.172**
0.049**
(1.858)
(1.754)
0.073
0.080
(0.766)
(0.860)
6.919**
0.186**
(2.295)
(2.107)
0.101
0.089
(1.056)
(0.950)
-0.063
0.090
(-0.681)
(0.828)
0.074
Employee training x Equity capital
(0.614)
0.249***
Management development x Equity capital
(2.821)
0.035
Comparison of performance x Equity capital
(0.376)
0.150*
Formal planning x Equity capital
(1.328)
-0.063
Networking x Equity capital R2 R2 (Adjusted) F- value ∆R2 Max VIF
(-0.651)
0.121 15.218*** 1.108
0.188 0.164 7.728*** 0.028 1.053
0.222 0.199 9.516*** .035 1.005
N= 104, t values are in parentheses *** Significant at the 99% confidence interval (one-tailed) ** Significant at the 95% confidence interval (one-tailed) * Significant at the 90% confidence interval (one-tailed)
16
Plot of Moderating Effects on Equity Capital is provided below in Figure F-1
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FIGURE F-1: PLOT OF MODERATING EFFECTS OF EQUITY CAPITAL
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Appendix G The Generational Effects of Organisational Learning on Innovation and Firm Performance TABLE G-1 ORGANISATIONAL LEARNING AND INNOVATION IN FAMILY SMES 1st Generation
2nd + Generation
All
Employee training
0.033 (0.211)
0.176 (1.044)
(1.071)
Management development
0.006 (0.039)
0.049 (0.388)
(0.308)
Comparison of performance
0.213 (1.240)
0.043 (0.300)
(0.963)
Formal planning
0.184 (1.211)
0.015 (0.116)
(0.410)
Networks
0.279** (1.918)
0.092 (0.743)
0.179**
-0.307** (-2.111)
-0.302*** (-2.457)
-0.296***
Firm age
249** (1.725)
-0.132 (-1.053)
Past performance
-0.361** (-2.441)
0.043 (0.346)
-0.124*
Intercept
9.490*** (2.817)
9.679*** (3.583)
9.286***
R square
0.178
0.091
0.129
F-value
4.227**
6.035**
7.508***
1.106
1.045
1.104
Independent variables: 0.107 0.029 0.091 0.040
(1.921)
Control variables: Firm size (Ln - employees)
Max VIF
(-3.180)
0.011 (0.120) (-1.307)
(4.359)
N= 104 (All), N = 42 (1st generation), N= 62 (2nd + generations), t values are in parentheses *** Significant at the 99% confidence interval (one-tailed) ** Significant at the 95% confidence interval (one-tailed) * Significant at the 90% confidence interval (one-tailed)
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TABLE G-2 ORGANISATIONAL LEARNING AND FIRM PERFORMANCE IN FAMILY SMES Firm Performance - SG Variables
All
1st Generation
2nd +Generation
Management development
0.044 (0.295) 0.286** (2.026)
-0. 057 (-0.469) 0. .034 (0. .280)
Comparison of performance
0.127 (0.852)
0.120 (1.012)
Formal planning
0.221* (1.558)
0.121 (1.019)
Networks
0.354** (2.354)
0.060 (0.495)
9..247** (1.707)
0. .390*** (2.890)
9.093 (0.628)
0. .122 (0. .979)
(1.023)
9.010 (0.070)
-0.045 (-0.366)
(-0.491)
-0.062 (-0.384)
0. .089 (0. .749)
0.923 (0.237)
0.405 (0.222)
(0.129)
R square
0.270
0.152
0.188
F-value
4.692***
10.749***
7.728***
1.012
1.115
1.136
Independent variable: Employee training
0.024 (0.233)
0.172** (1.8582)
0.073 (0.766)
0.212** (2.295)
0.101 (1.056)
Control variables: Innovation Firm size (Ln - employees) Firm age Past performance Intercept
Max VIF
0.354*** (3.923)
0.098 -0.045 0.123* (1.348)
0.273
N= 104 (All), N = 42 (1st generation), N= 62 (2nd +generations), t values are in parentheses *** Significant at the 99% confidence interval (one-tailed) ** Significant at the 95% confidence interval (one-tailed) * Significant at the 90% confidence interval (one-tailed
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